wall_yolo_segment.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install roboflow","metadata":{"execution":{"iopub.status.busy":"2023-08-02T14:17:35.486241Z","iopub.execute_input":"2023-08-02T14:17:35.486700Z","iopub.status.idle":"2023-08-02T14:18:03.323201Z","shell.execute_reply.started":"2023-08-02T14:17:35.486664Z","shell.execute_reply":"2023-08-02T14:18:03.321961Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting roboflow\n  Downloading roboflow-1.1.2-py3-none-any.whl (57 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.4/57.4 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta 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in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.0.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from roboflow) (2.31.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.16.0)\nCollecting supervision (from roboflow)\n  Downloading supervision-0.12.0-py3-none-any.whl (48 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.5/48.5 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: urllib3>=1.26.6 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.26.15)\nCollecting wget (from roboflow)\n  Downloading wget-3.2.zip (10 kB)\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.10/site-packages (from roboflow) (4.65.0)\nRequirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow) (6.0)\nRequirement already satisfied: requests-toolbelt in /opt/conda/lib/python3.10/site-packages (from roboflow) (0.10.1)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (1.1.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (4.40.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (21.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->roboflow) (3.1.0)\nRequirement already satisfied: opencv-python-headless<5.0.0.0,>=4.8.0.74 in /opt/conda/lib/python3.10/site-packages (from supervision->roboflow) (4.8.0.74)\nCollecting Pillow>=7.1.2 (from roboflow)\n  Downloading Pillow-8.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m78.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n\u001b[?25hBuilding wheels for collected packages: wget\n  Building wheel for wget (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=11447e80403e3d056c0fdabaf17d77c6669babee7eff36f674fcc46631325ce8\n  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\nSuccessfully built wget\nInstalling collected packages: wget, pyparsing, Pillow, idna, cycler, chardet, certifi, supervision, roboflow\n  Attempting uninstall: pyparsing\n    Found existing installation: pyparsing 3.0.9\n    Uninstalling pyparsing-3.0.9:\n      Successfully uninstalled pyparsing-3.0.9\n  Attempting uninstall: Pillow\n    Found existing installation: Pillow 9.5.0\n    Uninstalling Pillow-9.5.0:\n      Successfully uninstalled Pillow-9.5.0\n  Attempting uninstall: idna\n    Found existing installation: idna 3.4\n    Uninstalling idna-3.4:\n      Successfully uninstalled idna-3.4\n  Attempting uninstall: cycler\n    Found existing installation: cycler 0.11.0\n    Uninstalling cycler-0.11.0:\n      Successfully uninstalled cycler-0.11.0\n  Attempting uninstall: certifi\n    Found existing installation: certifi 2023.5.7\n    Uninstalling certifi-2023.5.7:\n      Successfully uninstalled certifi-2023.5.7\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\njupyterlab-lsp 4.2.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\nmomepy 0.6.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\npytoolconfig 1.2.5 requires packaging>=22.0, but you have packaging 21.3 which is incompatible.\nscikit-image 0.21.0 requires pillow>=9.0.1, but you have pillow 8.4.0 which is incompatible.\nydata-profiling 4.3.1 requires scipy<1.11,>=1.4.1, but you have scipy 1.11.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed Pillow-8.4.0 certifi-2022.12.7 chardet-4.0.0 cycler-0.10.0 idna-2.10 pyparsing-2.4.7 roboflow-1.1.2 supervision-0.12.0 wget-3.2\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install ultralytics","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:27:30.038728Z","iopub.execute_input":"2023-08-02T17:27:30.039867Z","iopub.status.idle":"2023-08-02T17:27:41.838737Z","shell.execute_reply.started":"2023-08-02T17:27:30.039823Z","shell.execute_reply":"2023-08-02T17:27:41.837512Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Requirement already satisfied: ultralytics in /opt/conda/lib/python3.10/site-packages (8.0.134)\nRequirement already satisfied: matplotlib>=3.2.2 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (3.7.1)\nRequirement already satisfied: opencv-python>=4.6.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (4.8.0.74)\nRequirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (8.4.0)\nRequirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (6.0)\nRequirement already satisfied: requests>=2.23.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (2.31.0)\nRequirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (1.11.1)\nRequirement already satisfied: torch>=1.7.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (2.0.0)\nRequirement already satisfied: torchvision>=0.8.1 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (0.15.1)\nRequirement already satisfied: tqdm>=4.64.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (4.65.0)\nRequirement already satisfied: pandas>=1.1.4 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (1.5.3)\nRequirement already satisfied: seaborn>=0.11.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (0.12.2)\nRequirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from ultralytics) (5.9.3)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (0.10.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (4.40.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (1.4.4)\nRequirement already satisfied: numpy>=1.20 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (1.23.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (21.3)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (2.4.5)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->ultralytics) (2.8.2)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.4->ultralytics) (2023.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (3.1.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (2.10)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (2022.12.7)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->ultralytics) (3.12.2)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->ultralytics) (4.6.3)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->ultralytics) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->ultralytics) (3.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->ultralytics) (3.1.2)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from cycler>=0.10->matplotlib>=3.2.2->ultralytics) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.7.0->ultralytics) (2.1.3)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.7.0->ultralytics) (1.3.0)\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install super-gradients","metadata":{"execution":{"iopub.status.busy":"2023-08-02T14:18:15.609679Z","iopub.execute_input":"2023-08-02T14:18:15.610317Z","iopub.status.idle":"2023-08-02T14:19:30.969696Z","shell.execute_reply.started":"2023-08-02T14:18:15.610279Z","shell.execute_reply":"2023-08-02T14:19:30.968419Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Collecting super-gradients\n  Downloading super_gradients-3.1.3-py3-none-any.whl (1.0 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: torch>=1.9.0 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (2.0.0)\nRequirement already satisfied: tqdm>=4.57.0 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (4.65.0)\nRequirement already satisfied: boto3>=1.17.15 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (1.26.100)\nRequirement already satisfied: jsonschema>=3.2.0 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (4.17.3)\nRequirement already satisfied: Deprecated>=1.2.11 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (1.2.14)\nRequirement already satisfied: opencv-python>=4.5.1 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (4.8.0.74)\nRequirement already satisfied: scipy>=1.6.1 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (1.11.1)\nRequirement already satisfied: matplotlib>=3.3.4 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (3.7.1)\nRequirement already satisfied: psutil>=5.8.0 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (5.9.3)\nRequirement already satisfied: tensorboard>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from super-gradients) (2.12.3)\nRequirement already satisfied: setuptools>=21.0.0 in /opt/conda/lib/python3.10/site-packages (from 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certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.5.0->sphinx~=4.0.2->super-gradients) (2022.12.7)\nCollecting pyproject_hooks (from build->pip-tools>=6.12.1->super-gradients)\n  Downloading pyproject_hooks-1.0.0-py3-none-any.whl (9.3 kB)\nCollecting humanfriendly>=9.1 (from coloredlogs->onnxruntime==1.13.1->super-gradients)\n  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich->onnx-simplifier<1.0,>=0.3.6->super-gradients) (2.2.0)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->onnxruntime==1.13.1->super-gradients) (1.3.0)\nRequirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->onnx-simplifier<1.0,>=0.3.6->super-gradients) (0.1.0)\nRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->super-gradients) (0.4.8)\nRequirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.4.1->super-gradients) (3.2.2)\nBuilding wheels for collected packages: pycocotools, termcolor, treelib, coverage, antlr4-python3-runtime, stringcase\n  Building wheel for pycocotools (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.6-cp310-cp310-linux_x86_64.whl size=101740 sha256=20f7b0ea30c49d3ca82652f03f0d57ace9ccf2e893d995ea498bf97290ba7d41\n  Stored in directory: /root/.cache/pip/wheels/58/e6/f9/f87c8f8be098b51b616871315318329cae12cdb618f4caac93\n  Building wheel for termcolor (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4848 sha256=96098cb1e8c693b885d6f43fb0e7e97fc5dfa56aaeea325dd30bed2440ca2036\n  Stored in directory: /root/.cache/pip/wheels/a1/49/46/1b13a65d8da11238af9616b00fdde6d45b0f95d9291bac8452\n  Building wheel for treelib (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for treelib: filename=treelib-1.6.1-py3-none-any.whl size=18384 sha256=76bdbe333649d3988cf5f1c72d4c0ceafda7ed972f8feb1820e95fc4eeeed2f1\n  Stored in directory: /root/.cache/pip/wheels/63/72/8b/76569b82bf280a03c4e294c3b29ee2398217186369c427ed4b\n  Building wheel for coverage (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for coverage: filename=coverage-5.3.1-cp310-cp310-linux_x86_64.whl size=210093 sha256=f4c0b3327daf26886b8845e0227bca3d026234efcee867da9d1c038eca1a6c7f\n  Stored in directory: /root/.cache/pip/wheels/e2/70/10/313be697f460d6024cfa94b7f0e22ffc1c53aab718fb4f42af\n  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144574 sha256=3139987d721caa0528682b36eb4df2eb4eb46d7dbb7ef1ab43ef25c8d3b2688f\n  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n  Building wheel for stringcase (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for stringcase: filename=stringcase-1.2.0-py3-none-any.whl size=3588 sha256=3db2762f0fcd6a33dd90bc04db42f2b502b660885493e0ab8a91d890192947a1\n  Stored in directory: /root/.cache/pip/wheels/31/ba/22/1a2d952a9ce8aa86e42fda41e2c87fdaf20e238c88bf8df013\nSuccessfully built pycocotools termcolor treelib coverage antlr4-python3-runtime stringcase\nInstalling collected packages: termcolor, stringcase, json-tricks, einops, antlr4-python3-runtime, treelib, sphinxcontrib-serializinghtml, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, pyproject_hooks, pyparsing, pyDeprecate, omegaconf, numpy, imagesize, humanfriendly, docutils, coverage, alabaster, onnx, coloredlogs, torchmetrics, sphinx, onnxruntime, onnx-simplifier, hydra-core, build, pycocotools, pip-tools, super-gradients\n  Attempting uninstall: termcolor\n    Found existing installation: termcolor 2.3.0\n    Uninstalling termcolor-2.3.0:\n      Successfully uninstalled termcolor-2.3.0\n  Attempting uninstall: pyparsing\n    Found existing installation: pyparsing 2.4.7\n    Uninstalling pyparsing-2.4.7:\n      Successfully uninstalled pyparsing-2.4.7\n  Attempting uninstall: numpy\n    Found existing installation: numpy 1.23.5\n    Uninstalling numpy-1.23.5:\n      Successfully uninstalled numpy-1.23.5\n  Attempting uninstall: docutils\n    Found existing installation: docutils 0.20.1\n    Uninstalling docutils-0.20.1:\n      Successfully uninstalled docutils-0.20.1\n  Attempting uninstall: onnx\n    Found existing installation: onnx 1.14.0\n    Uninstalling onnx-1.14.0:\n      Successfully uninstalled onnx-1.14.0\n  Attempting uninstall: torchmetrics\n    Found existing installation: torchmetrics 1.0.0\n    Uninstalling torchmetrics-1.0.0:\n      Successfully uninstalled torchmetrics-1.0.0\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\ncudf 23.6.1 requires cupy-cuda11x>=12.0.0, which is not installed.\ncuml 23.6.0 requires cupy-cuda11x>=12.0.0, which is not installed.\ndask-cudf 23.6.1 requires cupy-cuda11x>=12.0.0, which is not installed.\napache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.6 which is incompatible.\napache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 11.0.0 which is incompatible.\nchex 0.1.81 requires numpy>=1.25.0, but you have numpy 1.23.0 which is incompatible.\ncudf 23.6.1 requires protobuf<4.22,>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\ncuml 23.6.0 requires dask==2023.3.2, but you have dask 2023.7.0 which is incompatible.\ndask-cuda 23.6.0 requires dask==2023.3.2, but you have dask 2023.7.0 which is incompatible.\ndask-cudf 23.6.1 requires dask==2023.3.2, but you have dask 2023.7.0 which is incompatible.\nmomepy 0.6.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\npymc3 3.11.5 requires numpy<1.22.2,>=1.15.0, but you have numpy 1.23.0 which is incompatible.\npymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.11.1 which is incompatible.\npytoolconfig 1.2.5 requires packaging>=22.0, but you have packaging 21.3 which is incompatible.\nraft-dask 23.6.2 requires dask==2023.3.2, but you have dask 2023.7.0 which is incompatible.\nroboflow 1.1.2 requires pyparsing==2.4.7, but you have pyparsing 2.4.5 which is incompatible.\nscikit-image 0.21.0 requires pillow>=9.0.1, but you have pillow 8.4.0 which is incompatible.\nydata-profiling 4.3.1 requires scipy<1.11,>=1.4.1, but you have scipy 1.11.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed alabaster-0.7.13 antlr4-python3-runtime-4.9.3 build-0.10.0 coloredlogs-15.0.1 coverage-5.3.1 docutils-0.17.1 einops-0.3.2 humanfriendly-10.0 hydra-core-1.3.2 imagesize-1.4.1 json-tricks-3.16.1 numpy-1.23.0 omegaconf-2.3.0 onnx-1.13.0 onnx-simplifier-0.4.33 onnxruntime-1.13.1 pip-tools-7.1.0 pyDeprecate-0.3.2 pycocotools-2.0.6 pyparsing-2.4.5 pyproject_hooks-1.0.0 sphinx-4.0.3 sphinxcontrib-applehelp-1.0.4 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.1 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 sphinxcontrib-serializinghtml-1.1.5 stringcase-1.2.0 super-gradients-3.1.3 termcolor-1.1.0 torchmetrics-0.8.0 treelib-1.6.1\n","output_type":"stream"}]},{"cell_type":"code","source":"from roboflow import Roboflow\nrf = Roboflow(api_key=\"tceWcwxUL7Vbum8FWvhM\")\nproject = rf.workspace(\"sanjivani2\").project(\"1200-images\")\ndataset = project.version(1).download(\"yolov8\")","metadata":{"execution":{"iopub.status.busy":"2023-08-02T14:23:37.620696Z","iopub.execute_input":"2023-08-02T14:23:37.621074Z","iopub.status.idle":"2023-08-02T14:24:05.856294Z","shell.execute_reply.started":"2023-08-02T14:23:37.621043Z","shell.execute_reply":"2023-08-02T14:24:05.853979Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"loading Roboflow workspace...\nloading Roboflow project...\n[WARNING] we noticed you are downloading a `yolov8` datasets but you don't have `ultralytics` installed. Roboflow `.deploy` supports only models trained with `ultralytics==8.0.134`, to intall it `pip install ultralytics==8.0.134`.\nDownloading Dataset Version Zip in 1200-images-1 to yolov8: 100% [157516434 / 157516434] bytes\n","output_type":"stream"},{"name":"stderr","text":"Extracting Dataset Version Zip to 1200-images-1 in yolov8:: 100%|██████████| 3512/3512 [00:01<00:00, 2721.88it/s]\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)","Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m rf \u001b[38;5;241m=\u001b[39m Roboflow(api_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtceWcwxUL7Vbum8FWvhM\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m project \u001b[38;5;241m=\u001b[39m rf\u001b[38;5;241m.\u001b[39mworkspace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msanjivani2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mproject(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1200-images\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mproject\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mversion\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43myolov8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/roboflow/core/version.py:222\u001b[0m, in \u001b[0;36mVersion.download\u001b[0;34m(self, model_format, location, overwrite)\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__download_zip(link, location, model_format)\n\u001b[1;32m    221\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__extract_zip(location, model_format)\n\u001b[0;32m--> 222\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__reformat_yaml\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_format\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Dataset(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mversion, model_format, os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mabspath(location))\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/roboflow/core/version.py:743\u001b[0m, in \u001b[0;36mVersion.__reformat_yaml\u001b[0;34m(self, location, format)\u001b[0m\n\u001b[1;32m    740\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m content\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myolov5pytorch\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmt-yolov6\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myolov7pytorch\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myolov8\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 743\u001b[0m     \u001b[43mamend_data_yaml\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_yaml_callback\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/roboflow/util/annotations.py:10\u001b[0m, in \u001b[0;36mamend_data_yaml\u001b[0;34m(path, callback)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(path) \u001b[38;5;28;01mas\u001b[39;00m source:\n\u001b[1;32m      9\u001b[0m     content \u001b[38;5;241m=\u001b[39m yaml\u001b[38;5;241m.\u001b[39msafe_load(source)\n\u001b[0;32m---> 10\u001b[0m content \u001b[38;5;241m=\u001b[39m \u001b[43mcallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m os\u001b[38;5;241m.\u001b[39mremove(path)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m target:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/roboflow/core/version.py:732\u001b[0m, in \u001b[0;36mVersion.__reformat_yaml.<locals>.data_yaml_callback\u001b[0;34m(content)\u001b[0m\n\u001b[1;32m    729\u001b[0m     content[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m location \u001b[38;5;241m+\u001b[39m content[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mlstrip(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m..\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    730\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    731\u001b[0m     \u001b[38;5;66;03m# get_wrong_dependencies_versions raises exception if ultralytics is not installed at all\u001b[39;00m\n\u001b[0;32m--> 732\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myolov8\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mget_wrong_dependencies_versions\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    733\u001b[0m \u001b[43m   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is_git_dir,\n\u001b[1;32m     16\u001b[0m                                     is_pip_package)\n\u001b[1;32m     18\u001b[0m PREFIX \u001b[38;5;241m=\u001b[39m colorstr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUltralytics HUB: \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     19\u001b[0m HELP_MSG \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIf this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v8\n\u001b[1;32m      5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mv8\u001b[39m\u001b[38;5;124m'\u001b[39m,  \u001b[38;5;66;03m# tuple or list\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m classify, detect, pose, segment\n\u001b[1;32m      5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclassify\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msegment\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdetect\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpose\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/classify/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredict\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationPredictor, predict\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrain\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationTrainer, train\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationValidator, val\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/classify/predict.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredictor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BasePredictor\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresults\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Results\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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attempt_load_one_weight,\n\u001b[1;32m      4\u001b[0m                     attempt_load_weights, guess_model_scale, guess_model_task, parse_model, torch_safe_load,\n\u001b[1;32m      5\u001b[0m                     yaml_model_load)\n\u001b[1;32m      7\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattempt_load_one_weight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattempt_load_weights\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparse_model\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myaml_model_load\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mguess_model_task\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      8\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mguess_model_scale\u001b[39m\u001b[38;5;124m'\u001b[39m, 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\u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodules\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AIFI, C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x,\n\u001b[1;32m     11\u001b[0m                                     Classify, Concat, Conv, Conv2, ConvTranspose, Detect, DWConv, DWConvTranspose2d,\n\u001b[1;32m     12\u001b[0m                                     Focus, GhostBottleneck, GhostConv, HGBlock, HGStem, Pose, RepC3, RepConv,\n\u001b[1;32m     13\u001b[0m                                     RTDETRDecoder, Segment)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchecks\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_requirements, check_suffix, check_yaml\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/nn/modules/__init__.py:20\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblock\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (C1, C2, C3, C3TR, DFL, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, GhostBottleneck,\n\u001b[1;32m     17\u001b[0m                     HGBlock, HGStem, Proto, RepC3)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconv\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (CBAM, ChannelAttention, Concat, Conv, Conv2, ConvTranspose, DWConv, DWConvTranspose2d, Focus,\n\u001b[1;32m     19\u001b[0m                    GhostConv, LightConv, RepConv, SpatialAttention)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhead\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Classify, Detect, Pose, RTDETRDecoder, Segment\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransformer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AIFI, MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer, LayerNorm2d,\n\u001b[1;32m     22\u001b[0m                           MLPBlock, MSDeformAttn, TransformerBlock, TransformerEncoderLayer, TransformerLayer)\n\u001b[1;32m     24\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConv2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLightConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRepConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDWConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDWConvTranspose2d\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConvTranspose\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFocus\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     25\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGhostConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChannelAttention\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpatialAttention\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCBAM\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConcat\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransformerLayer\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     26\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransformerBlock\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMLPBlock\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLayerNorm2d\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDFL\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHGBlock\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHGStem\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPP\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPPF\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     27\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC2f\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3x\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3TR\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3Ghost\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGhostBottleneck\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBottleneck\u001b[39m\u001b[38;5;124m'\u001b[39m, 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------------------------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m    764\u001b[0m \n\u001b[1;32m    765\u001b[0m \u001b[38;5;66;03m# Check first-install steps\u001b[39;00m\n\u001b[1;32m    766\u001b[0m PREFIX \u001b[38;5;241m=\u001b[39m colorstr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUltralytics: \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 767\u001b[0m SETTINGS \u001b[38;5;241m=\u001b[39m \u001b[43mget_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    768\u001b[0m DATASETS_DIR \u001b[38;5;241m=\u001b[39m Path(SETTINGS[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatasets_dir\u001b[39m\u001b[38;5;124m'\u001b[39m])  \u001b[38;5;66;03m# global datasets directory\u001b[39;00m\n\u001b[1;32m    769\u001b[0m ENVIRONMENT \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mColab\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_colab() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKaggle\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_kaggle() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJupyter\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_jupyter() \u001b[38;5;28;01melse\u001b[39;00m \\\n\u001b[1;32m    770\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDocker\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_docker() \u001b[38;5;28;01melse\u001b[39;00m platform\u001b[38;5;241m.\u001b[39msystem()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/utils/__init__.py:700\u001b[0m, in \u001b[0;36mget_settings\u001b[0;34m(file, version)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mhashlib\u001b[39;00m\n\u001b[1;32m    699\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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\u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/utils/torch_utils.py:18\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctional\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mF\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      3\u001b[0m     CarlaStereo,\n\u001b[1;32m      4\u001b[0m     CREStereo,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m     SintelStereo,\n\u001b[1;32m     13\u001b[0m )\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcaltech\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Caltech101, Caltech256\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/datasets/_optical_flow.py:12\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _read_png_16\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _read_pfm, verify_str_arg\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VisionDataset\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/io/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, Iterator\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _log_api_usage_once\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_load_gpu_decoder\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _HAS_GPU_VIDEO_DECODER\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/utils.py:11\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageColor, ImageDraw, ImageFont\n\u001b[1;32m     13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmake_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msave_image\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflow_to_image\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     20\u001b[0m ]\n\u001b[1;32m     23\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mno_grad()\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_grid\u001b[39m(\n\u001b[1;32m     25\u001b[0m     tensor: Union[torch\u001b[38;5;241m.\u001b[39mTensor, List[torch\u001b[38;5;241m.\u001b[39mTensor]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m     33\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/PIL/ImageDraw.py:36\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumbers\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageColor, ImageFont\n\u001b[1;32m     38\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;124;03mA simple 2D drawing interface for PIL images.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;124;03m<p>\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;124;03mApplication code should use the <b>Draw</b> factory, instead of\u001b[39;00m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;124;03mdirectly.\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mImageDraw\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/PIL/ImageFont.py:35\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BytesIO\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, features\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_util\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m isDirectory, isPath\n\u001b[1;32m     37\u001b[0m LAYOUT_BASIC \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     38\u001b[0m LAYOUT_RAQM \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n","\u001b[0;31mImportError\u001b[0m: cannot import name 'isDirectory' from 'PIL._util' (/opt/conda/lib/python3.10/site-packages/PIL/_util.py)"],"ename":"ImportError","evalue":"cannot import name 'isDirectory' from 'PIL._util' (/opt/conda/lib/python3.10/site-packages/PIL/_util.py)","output_type":"error"}]},{"cell_type":"code","source":"# Start Weights & Biases (optional)\n%pip install -q wandb  #  use 'wandb disable'/'wandb enable' to turn off/on\nimport wandb\nfrom kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient() \n\npersonal_key_for_api = '68e8306948af360c729d0bb4693bbab0eaaa8046'\n\n! wandb login $personal_key_for_api","metadata":{"execution":{"iopub.status.busy":"2023-08-02T14:32:47.126843Z","iopub.execute_input":"2023-08-02T14:32:47.127225Z","iopub.status.idle":"2023-08-02T14:33:03.178542Z","shell.execute_reply.started":"2023-08-02T14:32:47.127196Z","shell.execute_reply":"2023-08-02T14:33:03.177321Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Note: you may need to restart the kernel to use updated packages.\n\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","output_type":"stream"}]},{"cell_type":"code","source":"!yolo task=segment \\\nmode=train \\\nmodel=yolov8m-seg.pt \\\ndata=/kaggle/working/1200-images-1/data.yaml \\\nepochs=100 \\\nimgsz=640","metadata":{"execution":{"iopub.status.busy":"2023-08-02T14:37:44.927925Z","iopub.execute_input":"2023-08-02T14:37:44.928458Z","iopub.status.idle":"2023-08-02T17:16:53.292852Z","shell.execute_reply.started":"2023-08-02T14:37:44.928419Z","shell.execute_reply":"2023-08-02T17:16:53.291540Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\nDownloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt to yolov8m-seg.pt...\n100%|██████████████████████████████████████| 52.4M/52.4M [00:02<00:00, 21.0MB/s]\nNew https://pypi.org/project/ultralytics/8.0.146 available 😃 Update with 'pip install -U ultralytics'\nUltralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8m-seg.pt, data=/kaggle/working/1200-images-1/data.yaml, epochs=100, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/segment/train\nDownloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n100%|█████████████████████████████████████████| 755k/755k [00:00<00:00, 119MB/s]\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\nOverriding model.yaml nc=80 with nc=2\n\n                   from  n    params  module                                       arguments                     \n  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n 22        [15, 18, 21]  1   5160182  ultralytics.nn.modules.head.Segment          [2, 32, 192, [192, 384, 576]] \nYOLOv8m-seg summary: 331 layers, 27240806 parameters, 27240790 gradients\n\nTransferred 531/537 items from pretrained weights\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train', view at http://localhost:6006/\n\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msavioratharv2003\u001b[0m (\u001b[33msavior\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.15.8 is available!  To upgrade, please run:\n\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.5\n\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20230802_143809-ovtrchx5\u001b[0m\n\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33myouthful-capybara-19\u001b[0m\n\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/savior/YOLOv8\u001b[0m\n\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/savior/YOLOv8/runs/ovtrchx5\u001b[0m\n\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\nDownloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n100%|██████████████████████████████████████| 6.23M/6.23M [00:00<00:00, 28.5MB/s]\n\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/1200-images-1/train/labels... 1394 images, 2 bac\u001b[0m\n\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/1200-images-1/train/labels.cache\n\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/1200-images-1/valid/labels... 180 images, 0 backgr\u001b[0m\n\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /kaggle/working/1200-images-1/valid/labels.cache\nPlotting labels to runs/segment/train/labels.jpg... \n\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001667, momentum=0.9) with parameter groups 86 weight(decay=0.0), 97 weight(decay=0.0005), 96 bias(decay=0.0)\nImage sizes 640 train, 640 val\nUsing 2 dataloader workers\nLogging results to \u001b[1mruns/segment/train\u001b[0m\nStarting training for 100 epochs...\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      1/100      10.4G      1.044      2.274      1.545      1.273         25   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.539      0.391      0.449      0.291      0.401      0.282      0.289      0.141\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      2/100      10.6G      1.051       2.07      1.296      1.253         18   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.59      0.468      0.503      0.326      0.529      0.408      0.397      0.181\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      3/100      10.5G       1.07      2.038      1.283      1.262          9   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.728      0.457      0.571       0.39      0.678      0.412       0.49      0.267\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      4/100      10.6G      1.026       1.96      1.214      1.234         25   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.628      0.505      0.553      0.382      0.603      0.485      0.517      0.302\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      5/100      10.7G      0.984       1.87      1.178      1.223         20   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.482      0.443      0.462      0.328      0.436      0.455      0.444      0.263\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      6/100      10.6G     0.9553       1.84      1.161      1.205         13   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.747      0.531      0.639      0.468      0.734      0.499       0.59      0.361\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      7/100      10.6G     0.9098       1.79      1.111      1.181         29   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.696      0.563      0.655      0.479      0.717      0.545      0.629      0.401\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      8/100      10.6G      0.911      1.757      1.103      1.178         23   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.771      0.433      0.575      0.441      0.779      0.439      0.561      0.374\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n      9/100      10.6G      0.881      1.703      1.047      1.155         29   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.697      0.599      0.668      0.499       0.68      0.559      0.626      0.404\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     10/100      10.6G     0.8636       1.63      1.012      1.138         14   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.699      0.601      0.668      0.518      0.712      0.577      0.645      0.423\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     11/100      10.6G     0.8327       1.59     0.9856      1.131         11   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.702      0.532      0.629      0.496      0.722      0.498      0.608      0.362\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     12/100      10.6G     0.8314      1.622     0.9884      1.128         30   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.716       0.62      0.689      0.548      0.693      0.601      0.654      0.447\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     13/100      10.6G     0.8307      1.568      0.968      1.126         14   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.774      0.578      0.693      0.525      0.773      0.571      0.669      0.451\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     14/100      9.38G     0.8121      1.559     0.9478      1.115         30   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.705      0.611      0.703       0.53      0.785      0.553      0.668      0.443\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     15/100      10.6G     0.8018      1.521     0.9577      1.115         21   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.837      0.529       0.67      0.528      0.851      0.539      0.667      0.446\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     16/100      10.6G     0.8025       1.53     0.9201      1.108         39   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.736      0.683      0.753      0.598      0.787      0.638       0.74      0.511\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     17/100      10.7G      0.787      1.501     0.9371      1.107         45   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.738      0.583      0.673      0.534      0.812       0.53       0.65      0.458\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     18/100      10.7G      0.774      1.468     0.8982      1.099         48   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.769      0.614      0.725       0.59      0.769      0.614      0.719      0.498\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     19/100      10.6G     0.7628      1.411     0.8801      1.098         18   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.79      0.597      0.705      0.555      0.789      0.584       0.68      0.466\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     20/100      9.33G     0.7447      1.368     0.8601      1.081         21   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.765      0.637      0.735       0.59      0.776      0.646      0.723      0.486\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     21/100      10.6G     0.7399      1.426     0.8567      1.079         16   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.766      0.624      0.725      0.584      0.807      0.595      0.697      0.479\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     22/100      10.6G     0.7383      1.378     0.8423      1.078         23   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.813      0.607      0.729      0.594      0.809      0.605      0.722       0.51\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     23/100      10.6G     0.7263      1.336      0.828      1.067         16   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.759      0.661      0.759      0.608      0.755      0.644      0.739      0.512\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     24/100      10.6G     0.7247      1.369     0.8353      1.068          8   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.787      0.642      0.736      0.591      0.799       0.64      0.725      0.503\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     25/100      10.6G     0.7168      1.339     0.8174      1.067         22   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.776      0.661      0.757      0.612      0.787      0.654      0.748       0.52\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     26/100      10.7G     0.7123      1.295     0.7952       1.06         12   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.808      0.645      0.766       0.63      0.808      0.644      0.757      0.527\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     27/100      10.6G     0.7205      1.334     0.8166      1.061         21   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.756      0.672      0.752      0.602      0.744      0.652      0.737      0.508\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     28/100      10.6G     0.6868      1.287     0.7756      1.042         18   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.809      0.662      0.791      0.644      0.802      0.652      0.776      0.543\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     29/100      10.6G     0.6959      1.276     0.7774      1.045         42   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.809      0.655      0.775      0.632      0.839      0.649      0.762      0.528\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     30/100      10.7G     0.6866      1.242     0.7742      1.043         35   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.744      0.688      0.772      0.628      0.746      0.684      0.761      0.521\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     31/100      10.6G      0.683      1.286     0.7791      1.042         39   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.769      0.703      0.775      0.622      0.773      0.677      0.761      0.537\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     32/100      10.6G     0.6878      1.209     0.7708      1.045         25   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.778      0.693      0.786       0.64       0.77      0.675      0.758      0.531\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     33/100      10.6G     0.6711      1.235     0.7496      1.035         35   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.823      0.595      0.734      0.601      0.826      0.593      0.723      0.507\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     34/100      10.6G     0.6755      1.219     0.7476      1.031         51   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.773      0.693      0.782      0.652      0.791      0.671      0.765      0.549\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     35/100      10.6G     0.6586      1.199     0.7351       1.03         43   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.766      0.669      0.781      0.648      0.763      0.659      0.765      0.548\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     36/100      10.7G      0.656      1.177     0.7194       1.03         20   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.808      0.685      0.785      0.652      0.793      0.665      0.747      0.538\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     37/100      10.6G     0.6545      1.216     0.7282      1.026         38   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.782      0.717      0.798      0.671      0.772      0.701      0.782      0.556\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     38/100      10.7G     0.6497      1.178     0.7088      1.018         46   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.778      0.705      0.789      0.634      0.773      0.686      0.766      0.544\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     39/100      10.6G     0.6515      1.199      0.703      1.023         20   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.787      0.725      0.795      0.662       0.81      0.697      0.777      0.549\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     40/100      10.6G     0.6419      1.166     0.6975       1.02         11   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.834      0.683      0.811      0.672      0.839      0.669      0.789      0.565\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     41/100      10.6G     0.6354      1.138     0.6872      1.014         16   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.827      0.711      0.818       0.68      0.819      0.694      0.798      0.574\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     42/100      10.6G     0.6275      1.091     0.6724      1.007         14   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.83      0.721      0.816      0.682       0.82      0.704      0.798      0.568\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     43/100      10.6G      0.633      1.108     0.6686      1.011         29   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.823      0.719      0.813      0.676       0.81      0.708      0.796      0.557\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     44/100      10.6G     0.6374      1.142     0.6878      1.013         53   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.829      0.721      0.816      0.687      0.835      0.694      0.804      0.567\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     45/100      10.6G     0.6162      1.091     0.6569      1.005         24   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.812      0.711      0.794      0.668      0.817      0.716      0.788      0.571\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     46/100      10.6G     0.6228       1.11     0.6651      1.003         32   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.811      0.725      0.821      0.689      0.805      0.723      0.808      0.574\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     47/100      10.6G     0.6205      1.081     0.6622      1.002         24   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.775      0.737      0.814      0.687      0.772      0.717      0.797      0.561\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     48/100      10.6G     0.6016      1.053     0.6333     0.9909         24   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.797      0.732      0.804      0.666      0.801      0.732      0.796      0.558\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     49/100      10.6G     0.6094      1.096     0.6333     0.9969         30   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.81      0.723      0.811      0.687       0.81      0.722      0.796      0.568\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     50/100      10.6G     0.6085       1.05     0.6246     0.9989         22   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.814       0.73      0.818      0.696      0.809      0.724      0.808      0.585\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     51/100      10.6G     0.5959      1.028     0.6229     0.9914         39   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.842      0.692      0.818      0.687       0.84      0.687      0.802      0.582\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     52/100      10.6G     0.5939      1.039     0.6104      0.983         44   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.778      0.729      0.822      0.688      0.778       0.72      0.805       0.58\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     53/100      10.6G     0.5967      1.055      0.619     0.9895         34   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.844       0.71      0.818      0.695       0.85      0.693      0.803      0.577\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     54/100      9.29G     0.5906      1.042     0.6032     0.9861         10   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.841       0.75      0.846      0.716      0.836      0.746      0.828      0.613\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     55/100      10.6G     0.5841      1.018     0.5892     0.9783         26   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.838      0.761      0.849      0.717      0.836      0.759      0.836      0.603\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     56/100      10.6G     0.5741      1.006     0.5812     0.9768         17   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.835      0.742      0.834      0.706      0.829      0.726       0.82      0.591\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     57/100      10.6G     0.5743     0.9937     0.5818     0.9791         31   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.823      0.749      0.832        0.7      0.819      0.745       0.82      0.589\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     58/100      10.6G     0.5843      1.002     0.5782     0.9785         34   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.829      0.764      0.842      0.715      0.827      0.768       0.83      0.589\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     59/100      10.6G     0.5822      1.013     0.5875     0.9782         22   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.849      0.743      0.843      0.712      0.843      0.737      0.827      0.595\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     60/100      9.36G     0.5767     0.9848     0.5783     0.9756         23   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.844       0.74      0.835      0.713      0.845      0.737      0.828      0.601\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     61/100      10.6G     0.5759      1.017     0.5857     0.9795         18   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.851      0.747      0.844      0.715      0.838      0.735      0.821      0.591\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     62/100      10.6G     0.5599     0.9629     0.5663     0.9715         15   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.826      0.765      0.849      0.727      0.876      0.728      0.836      0.609\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     63/100      10.7G     0.5528     0.9565     0.5521     0.9663         47   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.853      0.748      0.848      0.723      0.827      0.746      0.831      0.604\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     64/100      10.6G       0.56     0.9666     0.5544      0.967         10   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.816      0.746      0.837      0.708      0.857      0.722      0.827      0.599\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     65/100      10.6G     0.5601      0.948     0.5463     0.9676         12   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.834      0.757      0.847      0.726      0.829      0.734      0.836       0.61\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     66/100      10.6G     0.5471     0.9301     0.5346      0.967         28   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.831      0.737      0.836      0.721      0.834      0.743      0.832      0.605\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     67/100      10.6G     0.5462     0.9272     0.5345     0.9622         23   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.836      0.773       0.85      0.732      0.835      0.771      0.839      0.612\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     68/100      10.6G     0.5461     0.9162     0.5272     0.9568         29   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.829      0.781      0.843      0.721      0.845      0.746      0.825      0.601\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     69/100      10.6G     0.5394     0.9401     0.5186     0.9586         48   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.858      0.754      0.852       0.74      0.852       0.76      0.842      0.621\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     70/100      10.6G      0.541     0.9268     0.5257     0.9548         36   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.862      0.754      0.854       0.74      0.826      0.781       0.85       0.62\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     71/100      10.7G     0.5361     0.9119     0.5197     0.9599         28   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.848      0.765      0.854      0.736      0.843      0.762      0.839      0.623\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     72/100      10.6G     0.5295     0.8961     0.5119     0.9487         31   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.831      0.762      0.848      0.729      0.832      0.766      0.836      0.617\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     73/100      10.6G     0.5315     0.8954     0.5137      0.953         26   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.835      0.774      0.853      0.733      0.833      0.771      0.841      0.614\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     74/100      10.6G     0.5229     0.8904     0.5037      0.946         27   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.861       0.77      0.864      0.742      0.865      0.769      0.853      0.631\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     75/100      10.6G     0.5266     0.8873     0.4985     0.9498         20   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.85      0.763      0.854      0.739      0.865      0.752      0.852      0.627\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     76/100      10.6G      0.513     0.8757     0.4914     0.9482         23   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.819      0.793      0.847      0.731      0.834      0.781      0.838      0.626\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     77/100      10.6G     0.5212     0.8502     0.4941     0.9462         11   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.835        0.8      0.866      0.749      0.847       0.79      0.852      0.629\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     78/100      10.7G     0.5132     0.8709     0.4811     0.9406         21   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.854      0.791      0.865      0.747      0.855      0.788      0.855       0.63\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     79/100      9.34G     0.5108     0.8476     0.4774     0.9408         37   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.857        0.8      0.866      0.757      0.878      0.774      0.858      0.634\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     80/100      10.6G     0.5082     0.8536     0.4774     0.9426         21   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.851      0.793      0.871      0.754      0.877      0.777      0.864      0.632\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     81/100      10.6G     0.5017     0.8371     0.4672     0.9416         27   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.866       0.77       0.86      0.744      0.855      0.771      0.849      0.622\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     82/100      10.6G     0.5009     0.8195       0.47     0.9323         15   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.885      0.774      0.868      0.755      0.863        0.8      0.865      0.638\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     83/100      10.6G     0.4995     0.8396     0.4599     0.9348         22   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.875      0.788      0.868      0.754      0.883      0.786      0.856      0.633\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     84/100      10.6G     0.4957     0.8151     0.4596     0.9338         34   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.867      0.791       0.87      0.759      0.863      0.793      0.857      0.641\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     85/100      10.6G     0.4944     0.8175     0.4516     0.9327         27   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.831      0.811      0.863      0.752      0.847      0.815      0.861      0.637\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     86/100      10.6G     0.4898     0.8117     0.4612     0.9347          8   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.863      0.807      0.871      0.757      0.866      0.802       0.86      0.641\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     87/100      10.6G     0.4828     0.8066     0.4392     0.9274         18   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.87      0.784      0.864      0.752       0.87      0.791      0.856      0.631\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     88/100      10.6G     0.4878     0.7996     0.4418     0.9269         19   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.895      0.783       0.87      0.764      0.895      0.783      0.861       0.64\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     89/100      10.6G     0.4834     0.8021     0.4389     0.9272         15   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.873      0.791       0.87      0.764      0.874      0.793      0.862       0.64\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     90/100      10.6G      0.476     0.8037     0.4281     0.9212         35   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.885      0.793       0.87      0.762      0.886      0.794      0.863      0.641\nClosing dataloader mosaic\n\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     91/100      9.14G     0.4751     0.7694     0.4382     0.9129         14   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.865      0.799      0.871      0.764      0.876      0.797       0.86      0.638\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     92/100      9.14G     0.4573      0.703      0.378     0.9023          9   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.846      0.821      0.865      0.752      0.848      0.816      0.858       0.64\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     93/100      9.16G     0.4565     0.7063     0.3807     0.8974         11   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.886      0.788      0.871      0.758      0.887      0.789      0.865      0.642\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     94/100      9.14G     0.4565     0.6831     0.3858        0.9         11   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108       0.89       0.79      0.869      0.763       0.89      0.793      0.862      0.643\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     95/100      9.15G     0.4509     0.6859     0.3633     0.8957         12   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.873        0.8      0.868      0.758      0.875      0.801       0.86      0.641\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     96/100      9.15G     0.4363     0.6757      0.358     0.8946          5   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.874      0.805      0.872      0.764      0.875      0.805      0.867      0.644\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     97/100      9.15G     0.4318      0.692     0.3586     0.8889          6   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.881      0.798      0.872      0.765      0.887      0.804      0.868      0.646\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     98/100      9.15G     0.4304     0.6584      0.352     0.8904          7   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.872      0.813      0.876       0.77      0.882       0.81      0.871      0.651\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n     99/100      9.14G      0.439     0.6736     0.3462     0.8988          6   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.875      0.794      0.872      0.769      0.881        0.8      0.869      0.648\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n    100/100      9.14G     0.4262     0.6448     0.3388      0.888         16   \n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.864      0.817      0.876      0.768      0.879      0.802      0.866       0.65\n\n100 epochs completed in 2.616 hours.\nOptimizer stripped from runs/segment/train/weights/last.pt, 54.8MB\nOptimizer stripped from runs/segment/train/weights/best.pt, 54.8MB\n\nValidating runs/segment/train/weights/best.pt...\nUltralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\nYOLOv8m-seg summary (fused): 245 layers, 27223542 parameters, 0 gradients\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.879      0.807      0.876      0.771      0.884      0.805      0.872      0.651\n               ceiling        180        202      0.899      0.847      0.906      0.814      0.905      0.845      0.902      0.673\n                  wall        180        906      0.859      0.767      0.846      0.727      0.864      0.766      0.841       0.63\nSpeed: 2.6ms preprocess, 11.2ms inference, 0.0ms loss, 3.5ms postprocess per image\nResults saved to \u001b[1mruns/segment/train\u001b[0m\n\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n\u001b[34m\u001b[1mwandb\u001b[0m: \n\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n\u001b[34m\u001b[1mwandb\u001b[0m:                  lr/pg0 ▃████▇▇▇▇▇▆▆▆▆▆▅▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:                  lr/pg1 ▃████▇▇▇▇▇▆▆▆▆▆▅▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:                  lr/pg2 ▃████▇▇▇▇▇▆▆▆▆▆▅▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP50(B) ▁▃▄▃▄▅▆▆▆▆▆▇▆▆▇▇▇▇▇▇▇█▇▇▇▇██████████████\n\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP50(M) ▁▃▅▄▅▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇██████████████\n\u001b[34m\u001b[1mwandb\u001b[0m:     metrics/mAP50-95(B) ▁▂▄▃▄▄▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇█▇███████████\n\u001b[34m\u001b[1mwandb\u001b[0m:     metrics/mAP50-95(M) ▁▃▄▄▄▅▆▆▆▆▆▇▆▇▆▇▇▇▇▇▇▇▇▇▇▇▇█████████████\n\u001b[34m\u001b[1mwandb\u001b[0m:    metrics/precision(B) ▁▅▅▆▄▆▅▆▆▅▆▆▆▆▆▆▇▇▆▆▇▇▇▇▇▇▇▇▇▇▇▇████▇███\n\u001b[34m\u001b[1mwandb\u001b[0m:    metrics/precision(M) ▁▅▆▆▆▆▇▆▇▆▇▇▆▇▇▇▇▇▇▇▇▇▇▇▇█▇▇▇█▇█████▇███\n\u001b[34m\u001b[1mwandb\u001b[0m:       metrics/recall(B) ▁▂▃▂▃▄▆▅▅▅▅▅▆▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇██▇█▇██▇██\n\u001b[34m\u001b[1mwandb\u001b[0m:       metrics/recall(M) ▁▃▄▃▄▅▆▅▅▆▆▆▆▆▆▆▆▆▇▇▆▇▇▇▇▇▇▇▇▇█▇████████\n\u001b[34m\u001b[1mwandb\u001b[0m:            model/GFLOPs ▁\n\u001b[34m\u001b[1mwandb\u001b[0m:        model/parameters ▁\n\u001b[34m\u001b[1mwandb\u001b[0m: model/speed_PyTorch(ms) ▁\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/box_loss ██▇▆▅▅▅▅▄▄▄▄▄▄▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/cls_loss █▆▆▅▅▅▄▄▄▄▄▄▄▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/dfl_loss ██▇▆▅▅▅▅▄▄▄▄▄▄▄▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/seg_loss █▇▆▆▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/box_loss ██▆▅▅▄▄▄▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/cls_loss █▄▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/dfl_loss ██▅▅▄▄▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/seg_loss █▄▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n\u001b[34m\u001b[1mwandb\u001b[0m: \n\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n\u001b[34m\u001b[1mwandb\u001b[0m:                  lr/pg0 5e-05\n\u001b[34m\u001b[1mwandb\u001b[0m:                  lr/pg1 5e-05\n\u001b[34m\u001b[1mwandb\u001b[0m:                  lr/pg2 5e-05\n\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP50(B) 0.87586\n\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP50(M) 0.87151\n\u001b[34m\u001b[1mwandb\u001b[0m:     metrics/mAP50-95(B) 0.77068\n\u001b[34m\u001b[1mwandb\u001b[0m:     metrics/mAP50-95(M) 0.6514\n\u001b[34m\u001b[1mwandb\u001b[0m:    metrics/precision(B) 0.87889\n\u001b[34m\u001b[1mwandb\u001b[0m:    metrics/precision(M) 0.88442\n\u001b[34m\u001b[1mwandb\u001b[0m:       metrics/recall(B) 0.80682\n\u001b[34m\u001b[1mwandb\u001b[0m:       metrics/recall(M) 0.80547\n\u001b[34m\u001b[1mwandb\u001b[0m:            model/GFLOPs 0.0\n\u001b[34m\u001b[1mwandb\u001b[0m:        model/parameters 27240806\n\u001b[34m\u001b[1mwandb\u001b[0m: model/speed_PyTorch(ms) 16.39\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/box_loss 0.42616\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/cls_loss 0.33881\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/dfl_loss 0.88803\n\u001b[34m\u001b[1mwandb\u001b[0m:          train/seg_loss 0.64477\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/box_loss 0.52085\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/cls_loss 0.55625\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/dfl_loss 0.93685\n\u001b[34m\u001b[1mwandb\u001b[0m:            val/seg_loss 1.10513\n\u001b[34m\u001b[1mwandb\u001b[0m: \n\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33myouthful-capybara-19\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/savior/YOLOv8/runs/ovtrchx5\u001b[0m\n\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 37 media file(s), 1 artifact file(s) and 0 other file(s)\n\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230802_143809-ovtrchx5/logs\u001b[0m\n^C\n","output_type":"stream"}]},{"cell_type":"code","source":"!yolo task=segment \\\nmode=val \\\nmodel=/kaggle/working/runs/segment/train/weights/best.pt \\\ndata=/kaggle/working/1200-images-1/data.yaml","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:17:50.249082Z","iopub.execute_input":"2023-08-02T17:17:50.249497Z","iopub.status.idle":"2023-08-02T17:18:36.346627Z","shell.execute_reply.started":"2023-08-02T17:17:50.249465Z","shell.execute_reply":"2023-08-02T17:18:36.344134Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\nUltralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\nYOLOv8m-seg summary (fused): 245 layers, 27223542 parameters, 0 gradients\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/1200-images-1/valid/labels.cache... 180 images, 0 \u001b[0m\n                 Class     Images  Instances      Box(P          R      mAP50  m\n                   all        180       1108      0.866      0.814      0.878      0.771      0.876      0.815      0.867      0.648\n               ceiling        180        202       0.89      0.847      0.909      0.812      0.905      0.856      0.896      0.672\n                  wall        180        906      0.842      0.781      0.847       0.73      0.848      0.774      0.838      0.625\nSpeed: 2.1ms preprocess, 16.7ms inference, 0.0ms loss, 2.4ms postprocess per image\nResults saved to \u001b[1mruns/segment/val\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"!yolo task=segment \\\nmode=predict \\\nmodel=/kaggle/working/runs/segment/train/weights/best.pt \\\nconf=0.25 \\\nsource=/kaggle/working/1200-images-1/test/images","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:18:43.061546Z","iopub.execute_input":"2023-08-02T17:18:43.061946Z","iopub.status.idle":"2023-08-02T17:19:12.278040Z","shell.execute_reply.started":"2023-08-02T17:18:43.061915Z","shell.execute_reply":"2023-08-02T17:19:12.276779Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\nUltralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\nYOLOv8m-seg summary (fused): 245 layers, 27223542 parameters, 0 gradients\n\nimage 1/176 /kaggle/working/1200-images-1/test/images/0047_jpg.rf.7ffae91fa589cdc5108fba9a4c1b6682.jpg: 384x640 1 ceiling, 7 walls, 96.1ms\nimage 2/176 /kaggle/working/1200-images-1/test/images/0071_jpg.rf.5f97902c6285a280951f61198efb1f19.jpg: 448x640 1 ceiling, 5 walls, 85.4ms\nimage 3/176 /kaggle/working/1200-images-1/test/images/0074_jpg.rf.64883b7a1bfe80030b87b496be5bdbaa.jpg: 448x640 1 ceiling, 9 walls, 13.3ms\nimage 4/176 /kaggle/working/1200-images-1/test/images/0077_jpg.rf.b3394e484ecba1d8782b46a1d8af29fc.jpg: 448x640 1 ceiling, 7 walls, 13.4ms\nimage 5/176 /kaggle/working/1200-images-1/test/images/0082_jpg.rf.0435ace7587b607874b6bf821fb2c2b7.jpg: 480x640 7 walls, 85.1ms\nimage 6/176 /kaggle/working/1200-images-1/test/images/0089_jpg.rf.1a82325ae64b1938c79d06267afd3f1e.jpg: 640x480 4 walls, 82.5ms\nimage 7/176 /kaggle/working/1200-images-1/test/images/0132_jpg.rf.b354b10c421dedbd6a60f2ef1e74abb3.jpg: 480x640 4 ceilings, 4 walls, 15.9ms\nimage 8/176 /kaggle/working/1200-images-1/test/images/0184_jpeg.rf.ef0f714177ff40e16c326aa09c84dc48.jpg: 480x640 4 ceilings, 11 walls, 14.9ms\nimage 9/176 /kaggle/working/1200-images-1/test/images/0187_jpg.rf.bdf9570bfc54cb361c2e1cbdf99035db.jpg: 288x640 1 ceiling, 5 walls, 85.7ms\nimage 10/176 /kaggle/working/1200-images-1/test/images/0192_jpg.rf.7100f4f78ce87f2b56764933ad2e4df7.jpg: 480x640 1 ceiling, 3 walls, 15.3ms\nimage 11/176 /kaggle/working/1200-images-1/test/images/04-contrasting-colour-block-bedroom-walls-in-neo-mint-green-and-blush-pink_jpg.rf.c69f92d0db4a247c2254bbdf1333519e.jpg: 448x640 11 walls, 15.9ms\nimage 12/176 /kaggle/working/1200-images-1/test/images/1100000066_jpeg.rf.ce5f8416ac390f1e46c16bc74e0bbbfb.jpg: 512x640 1 ceiling, 4 walls, 83.7ms\nimage 13/176 /kaggle/working/1200-images-1/test/images/11102374-1_jpg.rf.ed5f5c2d35ecfc31bae93f1eb0367a7a.jpg: 448x640 3 ceilings, 6 walls, 14.0ms\nimage 14/176 /kaggle/working/1200-images-1/test/images/18730771_11_PropertyImage1643981605356_470_1080_jpg.rf.998c126c9740b0d439df467c9dcf543f.jpg: 448x640 5 ceilings, 3 walls, 13.3ms\nimage 15/176 /kaggle/working/1200-images-1/test/images/1900000165_jpg.rf.29b7476dff220e772ebe450c95b74803.jpg: 640x480 1 ceiling, 3 walls, 15.2ms\nimage 16/176 /kaggle/working/1200-images-1/test/images/190_jpg.rf.3c98ff651f2b0cc0ea55e28a21dd0750.jpg: 480x640 2 ceilings, 5 walls, 15.3ms\nimage 17/176 /kaggle/working/1200-images-1/test/images/1_bhk_apartment-for-rent-thane_west-Thane-living_room_jpg.rf.41b517f7e1c35a889a1ce78874f318eb.jpg: 480x640 1 ceiling, 4 walls, 14.7ms\nimage 18/176 /kaggle/working/1200-images-1/test/images/2100000132_jpg.rf.165bb1d15a20735fab518af029fd3452.jpg: 640x480 1 ceiling, 4 walls, 15.5ms\nimage 19/176 /kaggle/working/1200-images-1/test/images/2201c89314_jpg.rf.1bcc559387948006e4921e1752a139f0.jpg: 384x640 5 ceilings, 7 walls, 14.3ms\nimage 20/176 /kaggle/working/1200-images-1/test/images/221_jpg.rf.435346701ef02062e6c64e86fa491460.jpg: 480x640 3 ceilings, 4 walls, 15.3ms\nimage 21/176 /kaggle/working/1200-images-1/test/images/23477739_9_PropertyImage23686_470_1080_jpg.rf.97020d97b23e4c9926a7af0baf5d38e6.jpg: 512x640 1 ceiling, 5 walls, 15.4ms\nimage 22/176 /kaggle/working/1200-images-1/test/images/239_jpg.rf.d3d31df45be4a8943f7e5df9664ac6b8.jpg: 640x640 3 walls, 17.3ms\nimage 23/176 /kaggle/working/1200-images-1/test/images/2500000233_jpg.rf.2c9fef02581a739710d5df16cf022cfd.jpg: 480x640 1 ceiling, 3 walls, 15.3ms\nimage 24/176 /kaggle/working/1200-images-1/test/images/25200000066_jpg.rf.feed1616f8f0acbcb0a9579c8bb78f02.jpg: 320x640 6 walls, 82.0ms\nimage 25/176 /kaggle/working/1200-images-1/test/images/25200000165_jpg.rf.e17b127aae1ca8922abd2b041d91b3b0.jpg: 320x640 11 walls, 12.5ms\nimage 26/176 /kaggle/working/1200-images-1/test/images/25600000264_jpg.rf.c48565e9d420934100bab2c0c073afa3.jpg: 512x640 1 ceiling, 3 walls, 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/kaggle/working/1200-images-1/test/images/new_image_8_jpg.rf.ee0123df89759a6a8b4506f999795506.jpg: 640x384 2 ceilings, 2 walls, 82.5ms\nimage 140/176 /kaggle/working/1200-images-1/test/images/new_image_9_jpg.rf.c66b06e46c70f709a0dc3b49e5480cfa.jpg: 640x352 2 ceilings, 1 wall, 83.5ms\nimage 141/176 /kaggle/working/1200-images-1/test/images/pexels-photo-3975566.rf.871d006dbceb74ebaa3df3b6988360b8.jpg: 448x640 3 walls, 14.1ms\nimage 142/176 /kaggle/working/1200-images-1/test/images/pexels-photo-4064835_jpeg.rf.6a12c4b77dc9af6e125d74cd8b03252a.jpg: 576x640 1 wall, 85.4ms\nimage 143/176 /kaggle/working/1200-images-1/test/images/pexels-photo-4067759.rf.fbae04558739557638b1d0e75640a844.jpg: 640x448 2 walls, 14.7ms\nimage 144/176 /kaggle/working/1200-images-1/test/images/pexels-photo-4203101_jpeg.rf.7cfbbf11ea5a499e3518885f1e6b4d99.jpg: 640x448 1 wall, 13.6ms\nimage 145/176 /kaggle/working/1200-images-1/test/images/raw_jpg.rf.02b6623588e1a561f9456f6ef9579de7.jpg: 480x640 1 ceiling, 10 walls, 15.2ms\nimage 146/176 /kaggle/working/1200-images-1/test/images/remixproject4-fon-72_3_jpg.rf.48e0ab5ae74236a630c33315aa2f2980.jpg: 448x640 3 walls, 13.9ms\nimage 147/176 /kaggle/working/1200-images-1/test/images/room-images-with-computer-table_19.rf.ddc98eb13c7d9b39f65d93e48f1390b5.jpg: 640x640 8 walls, 17.3ms\nimage 148/176 /kaggle/working/1200-images-1/test/images/room-images-with-furniture_11.rf.975ac791827f01eee20442d779dc9569.jpg: 416x640 13 ceilings, 11 walls, 13.8ms\nimage 149/176 /kaggle/working/1200-images-1/test/images/room-images-with-table-on-wall_10.rf.0173b50093bd2ed456cfffdf5c9fdd9c.jpg: 448x640 4 walls, 14.1ms\nimage 150/176 /kaggle/working/1200-images-1/test/images/room-images-with-table-on-wall_12_jpeg.rf.57fd41b617733cfc6a417eedef8b985b.jpg: 640x608 4 walls, 16.7ms\nimage 151/176 /kaggle/working/1200-images-1/test/images/room-images-with-table-on-wall_16_jpeg.rf.8ee7103491d01e1ee721005342ab1dea.jpg: 640x640 9 walls, 17.4ms\nimage 152/176 /kaggle/working/1200-images-1/test/images/room-images-with-table-on-wall_20.rf.30d242f8017f5d4be26c4c9ec190ed55.jpg: 384x640 9 walls, 14.3ms\nimage 153/176 /kaggle/working/1200-images-1/test/images/room-images-with-table-on-wall_35_jpeg.rf.8cefa5fe98e9fc545a7b3eb80bfc7677.jpg: 384x640 8 walls, 13.6ms\nimage 154/176 /kaggle/working/1200-images-1/test/images/room-images-with-table-on-wall_36.rf.9186f35cb2b115ac20e167c55e8e066c.jpg: 480x640 1 ceiling, 6 walls, 15.4ms\nimage 155/176 /kaggle/working/1200-images-1/test/images/rooms-with-sofa-paintings-and-almirah_8.rf.fb4473bacb76e6d6b129b3e1e4dcc305.jpg: 512x640 1 ceiling, 4 walls, 15.8ms\nimage 156/176 /kaggle/working/1200-images-1/test/images/spacious-rooms_47_jpeg.rf.3f756c4358e26b91d98293c2854fc626.jpg: 480x640 8 ceilings, 3 walls, 15.3ms\nimage 157/176 /kaggle/working/1200-images-1/test/images/the-metropolitan-of-baltimore-baltimore-md-primary-photo_jpg.rf.bd2ab99e1ded57045987176c54216be0.jpg: 448x640 1 ceiling, 8 walls, 14.1ms\nimage 158/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_101_jpg.rf.63649500df8de5f774d8828c88106210.jpg: 448x640 1 ceiling, 6 walls, 13.4ms\nimage 159/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_20.rf.0b3e5014739a1198cf82d52c1938a4fb.jpg: 640x512 2 ceilings, 8 walls, 15.6ms\nimage 160/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_22_jpg.rf.429d78be3a435fb6e8f516a4618347a4.jpg: 640x480 1 ceiling, 4 walls, 15.2ms\nimage 161/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_36.rf.f6046903a974368f8d0b10bb30a866a0.jpg: 480x640 2 walls, 15.2ms\nimage 162/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_41_jpg.rf.715fddf73c5896abbe03ca189fdd6489.jpg: 480x640 8 walls, 14.6ms\nimage 163/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_43.rf.3ba0fe6824b96d78302e3665391f0850.jpg: 512x640 4 ceilings, 8 walls, 16.1ms\nimage 164/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_43_jpg.rf.9686db1501a68b5bccd1ec6452606464.jpg: 512x640 4 ceilings, 7 walls, 15.1ms\nimage 165/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_62_jpg.rf.7d4430a2392779e71d5614a02d70f27f.jpg: 480x640 1 ceiling, 12 walls, 15.2ms\nimage 166/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_65_jpg.rf.0c741e5d0a581d36e23406ec41d45d3a.jpg: 640x384 3 ceilings, 4 walls, 15.0ms\nimage 167/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_68.rf.2339390f02baf4958d115a00ce24c219.jpg: 480x640 4 walls, 15.2ms\nimage 168/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_83.rf.c2707ec8ebe92d47068caedd2d4b29c1.jpg: 448x640 4 walls, 14.0ms\nimage 169/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_84_jpg.rf.4ccd1722f9e7a501a2458d814005e36f.jpg: 640x640 3 ceilings, 4 walls, 17.3ms\nimage 170/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_88_jpg.rf.7795b9af55112d73dec88af53d2c08a0.jpg: 448x640 2 walls, 14.1ms\nimage 171/176 /kaggle/working/1200-images-1/test/images/wall100_rgb_93.rf.71b4b46f0b8a6d5fc0d91fb6fa654e29.jpg: 352x640 8 walls, 13.9ms\nimage 172/176 /kaggle/working/1200-images-1/test/images/wall_10.rf.0ab144b2fc264d2a6e1de3ce76b6216f.jpg: 448x640 3 ceilings, 2 walls, 14.0ms\nimage 173/176 /kaggle/working/1200-images-1/test/images/wall_21_jpg.rf.9b8c965c848e540290748875f4e7215c.jpg: 448x640 3 ceilings, 3 walls, 13.2ms\nimage 174/176 /kaggle/working/1200-images-1/test/images/wall_4.rf.b6051bc793277f8ed8a605d58d585061.jpg: 448x640 1 ceiling, 5 walls, 13.3ms\nimage 175/176 /kaggle/working/1200-images-1/test/images/walnut-creek-apartments-bedroom.rf.8c6373838781fb91dbd92838f078a90c.jpg: 384x640 1 ceiling, 7 walls, 14.4ms\nimage 176/176 /kaggle/working/1200-images-1/test/images/white-wall-craft_room.rf.391771769ab123616c682da2b4ba77c7.jpg: 448x640 2 ceilings, 4 walls, 15.1ms\nSpeed: 2.2ms preprocess, 22.4ms inference, 2.0ms postprocess per image at shape (1, 3, 448, 640)\nResults saved to \u001b[1mruns/segment/predict\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install roboflow\n\nfrom roboflow import Roboflow\nrf = Roboflow(api_key=\"XcR8eKcpUPfaGieuJlxO\")\nproject = rf.workspace(\"pothole-ipd\").project(\"ipd-pothole-detection\")\ndataset = project.version(5).download(\"coco\")\n","metadata":{"execution":{"iopub.status.busy":"2023-07-08T21:11:44.331137Z","iopub.execute_input":"2023-07-08T21:11:44.331881Z","iopub.status.idle":"2023-07-08T21:12:19.318353Z","shell.execute_reply.started":"2023-07-08T21:11:44.331837Z","shell.execute_reply":"2023-07-08T21:12:19.314018Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Requirement already satisfied: roboflow in /opt/conda/lib/python3.10/site-packages (1.1.0)\nRequirement already satisfied: certifi==2022.12.7 in /opt/conda/lib/python3.10/site-packages (from roboflow) (2022.12.7)\nRequirement already satisfied: chardet==4.0.0 in /opt/conda/lib/python3.10/site-packages (from roboflow) (4.0.0)\nRequirement already satisfied: cycler==0.10.0 in /opt/conda/lib/python3.10/site-packages (from roboflow) (0.10.0)\nRequirement already satisfied: idna==2.10 in /opt/conda/lib/python3.10/site-packages (from roboflow) (2.10)\nRequirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.4.4)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from roboflow) (3.6.3)\nRequirement already satisfied: numpy>=1.18.5 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.23.0)\nRequirement already satisfied: opencv-python>=4.1.2 in /opt/conda/lib/python3.10/site-packages (from roboflow) (4.7.0.72)\nRequirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.10/site-packages (from roboflow) (9.5.0)\nCollecting pyparsing==2.4.7 (from roboflow)\n  Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\nRequirement already satisfied: python-dateutil in /opt/conda/lib/python3.10/site-packages (from roboflow) (2.8.2)\nRequirement already satisfied: python-dotenv in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.0.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from roboflow) (2.28.2)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.16.0)\nRequirement already satisfied: supervision in /opt/conda/lib/python3.10/site-packages (from roboflow) (0.11.1)\nRequirement already satisfied: urllib3>=1.26.6 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.26.15)\nRequirement already satisfied: wget in /opt/conda/lib/python3.10/site-packages (from roboflow) (3.2)\nRequirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.10/site-packages (from roboflow) (4.64.1)\nRequirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow) (5.4.1)\nRequirement already satisfied: requests-toolbelt in /opt/conda/lib/python3.10/site-packages (from roboflow) (0.10.1)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (1.0.7)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (4.39.3)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (21.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->roboflow) (2.1.1)\nInstalling collected packages: pyparsing\n  Attempting uninstall: pyparsing\n    Found existing installation: pyparsing 2.4.5\n    Uninstalling pyparsing-2.4.5:\n      Successfully uninstalled pyparsing-2.4.5\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\npytoolconfig 1.2.5 requires packaging>=22.0, but you have packaging 21.3 which is incompatible.\nsuper-gradients 3.1.2 requires pyparsing==2.4.5, but you have pyparsing 2.4.7 which is incompatible.\nydata-profiling 4.1.2 requires scipy<1.10,>=1.4.1, but you have scipy 1.10.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed pyparsing-2.4.7\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\nloading Roboflow workspace...\nloading Roboflow project...\nDownloading Dataset Version Zip in IPD-Pothole-Detection-5 to coco: 100% [160437101 / 160437101] bytes\n","output_type":"stream"},{"name":"stderr","text":"Extracting Dataset Version Zip to IPD-Pothole-Detection-5 in coco:: 100%|██████████| 2575/2575 [00:00<00:00, 2761.61it/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install roboflow==1.1.0\n\nfrom roboflow import Roboflow\nrf = Roboflow(api_key=\"XcR8eKcpUPfaGieuJlxO\")\nproject = rf.workspace(\"pothole-ipd\").project(\"personal-j9my0\")\ndataset = project.version(1).download(\"coco-segmentation\")\n","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:29:37.444059Z","iopub.execute_input":"2023-08-02T17:29:37.444474Z","iopub.status.idle":"2023-08-02T17:30:08.539500Z","shell.execute_reply.started":"2023-08-02T17:29:37.444445Z","shell.execute_reply":"2023-08-02T17:30:08.538166Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Collecting roboflow==1.1.0\n  Downloading roboflow-1.1.0-py3-none-any.whl (57 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.0/57.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: certifi==2022.12.7 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (2022.12.7)\nRequirement already satisfied: chardet==4.0.0 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (4.0.0)\nRequirement already satisfied: cycler==0.10.0 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (0.10.0)\nRequirement already satisfied: idna==2.10 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (2.10)\nRequirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (1.4.4)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (3.7.1)\nRequirement already satisfied: numpy>=1.18.5 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (1.23.0)\nRequirement already satisfied: opencv-python>=4.1.2 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (4.8.0.74)\nRequirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (8.4.0)\nCollecting pyparsing==2.4.7 (from roboflow==1.1.0)\n  Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\nRequirement already satisfied: python-dateutil in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (2.8.2)\nRequirement already satisfied: python-dotenv in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (1.0.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (2.31.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (1.16.0)\nRequirement already satisfied: supervision in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (0.12.0)\nRequirement already satisfied: urllib3>=1.26.6 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (1.26.15)\nRequirement already satisfied: wget in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (3.2)\nRequirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (4.65.0)\nRequirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (6.0)\nRequirement already satisfied: requests-toolbelt in /opt/conda/lib/python3.10/site-packages (from roboflow==1.1.0) (0.10.1)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow==1.1.0) (1.1.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow==1.1.0) (4.40.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow==1.1.0) (21.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->roboflow==1.1.0) (3.1.0)\nRequirement already satisfied: opencv-python-headless<5.0.0.0,>=4.8.0.74 in /opt/conda/lib/python3.10/site-packages (from supervision->roboflow==1.1.0) (4.8.0.74)\nInstalling collected packages: pyparsing, roboflow\n  Attempting uninstall: pyparsing\n    Found existing installation: pyparsing 2.4.5\n    Uninstalling pyparsing-2.4.5:\n      Successfully uninstalled pyparsing-2.4.5\n  Attempting uninstall: roboflow\n    Found existing installation: roboflow 1.1.2\n    Uninstalling roboflow-1.1.2:\n      Successfully uninstalled roboflow-1.1.2\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\npytoolconfig 1.2.5 requires packaging>=22.0, but you have packaging 21.3 which is incompatible.\nscikit-image 0.21.0 requires pillow>=9.0.1, but you have pillow 8.4.0 which is incompatible.\nsuper-gradients 3.1.3 requires pyparsing==2.4.5, but you have pyparsing 2.4.7 which is incompatible.\nydata-profiling 4.3.1 requires scipy<1.11,>=1.4.1, but you have scipy 1.11.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed pyparsing-2.4.7 roboflow-1.1.0\nloading Roboflow workspace...\nloading Roboflow project...\nDownloading Dataset Version Zip in personal-1 to coco-segmentation: 100% [78404307 / 78404307] bytes\n","output_type":"stream"},{"name":"stderr","text":"Extracting Dataset Version Zip to personal-1 in coco-segmentation:: 100%|██████████| 1871/1871 [00:00<00:00, 2177.41it/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install pillow==9.0.1","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:30:46.648700Z","iopub.execute_input":"2023-08-02T17:30:46.649091Z","iopub.status.idle":"2023-08-02T17:30:59.202544Z","shell.execute_reply.started":"2023-08-02T17:30:46.649058Z","shell.execute_reply":"2023-08-02T17:30:59.201259Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Collecting pillow==9.0.1\n  Downloading Pillow-9.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m54.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: pillow\n  Attempting uninstall: pillow\n    Found existing installation: Pillow 8.4.0\n    Uninstalling Pillow-8.4.0:\n      Successfully uninstalled Pillow-8.4.0\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nsuper-gradients 3.1.3 requires pyparsing==2.4.5, but you have pyparsing 2.4.7 which is incompatible.\nsupervision 0.12.0 requires pillow<9.0.0,>=8.4.0, but you have pillow 9.0.1 which is incompatible.\nydata-profiling 4.3.1 requires scipy<1.11,>=1.4.1, but you have scipy 1.11.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed pillow-9.0.1\n","output_type":"stream"}]},{"cell_type":"code","source":"dataset.version","metadata":{"execution":{"iopub.status.busy":"2023-07-08T21:12:19.322255Z","iopub.execute_input":"2023-07-08T21:12:19.322867Z","iopub.status.idle":"2023-07-08T21:12:19.331151Z","shell.execute_reply.started":"2023-07-08T21:12:19.322824Z","shell.execute_reply":"2023-07-08T21:12:19.330248Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"'5'"},"metadata":{}}]},{"cell_type":"code","source":"import cv2\nfrom ultralytics import YOLO\n\n# Load a pretrained YOLOv8n model\nmodel = YOLO('/kaggle/working/runs/segment/train/weights/best.pt')\n\n# Read an image using OpenCV\nsource = cv2.imread('https://images.pexels.com/photos/1571460/pexels-photo-1571460.jpeg?cs=srgb&dl=pexels-vecislavas-popa-1571460.jpg&fm=jpg')\n\n# Run inference on the source\nresults = model(source)  # list of Results objects","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:34:58.670035Z","iopub.execute_input":"2023-08-02T17:34:58.670451Z","iopub.status.idle":"2023-08-02T17:35:00.037227Z","shell.execute_reply.started":"2023-08-02T17:34:58.670421Z","shell.execute_reply":"2023-08-02T17:35:00.035033Z"},"trusted":true},"execution_count":27,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)","Cell \u001b[0;32mIn[27], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m YOLO\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Load a pretrained YOLOv8n model\u001b[39;00m\n\u001b[1;32m      5\u001b[0m model \u001b[38;5;241m=\u001b[39m YOLO(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/working/runs/segment/train/weights/best.pt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m8.0.134\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m start\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrtdetr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RTDETR\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msam\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SAM\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/hub/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauth\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Auth\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PREFIX\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HUBDatasetStats\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/hub/auth.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HUB_API_ROOT, PREFIX, request_with_credentials\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LOGGER, SETTINGS, emojis, is_colab, set_settings\n\u001b[1;32m      8\u001b[0m API_KEY_URL \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://hub.ultralytics.com/settings?tab=api+keys\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/hub/utils.py:14\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM_BAR_FORMAT,\n\u001b[1;32m     15\u001b[0m                                     TryExcept, __version__, colorstr, get_git_origin_url, is_colab, is_git_dir,\n\u001b[1;32m     16\u001b[0m                                     is_pip_package)\n\u001b[1;32m     18\u001b[0m PREFIX \u001b[38;5;241m=\u001b[39m colorstr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUltralytics HUB: \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     19\u001b[0m HELP_MSG \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIf this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v8\n\u001b[1;32m      5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mv8\u001b[39m\u001b[38;5;124m'\u001b[39m,  \u001b[38;5;66;03m# tuple or list\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m classify, detect, pose, segment\n\u001b[1;32m      5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclassify\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msegment\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdetect\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpose\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/classify/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredict\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationPredictor, predict\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrain\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationTrainer, train\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationValidator, val\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/classify/predict.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredictor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BasePredictor\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresults\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Results\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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attempt_load_one_weight,\n\u001b[1;32m      4\u001b[0m                     attempt_load_weights, guess_model_scale, guess_model_task, parse_model, torch_safe_load,\n\u001b[1;32m      5\u001b[0m                     yaml_model_load)\n\u001b[1;32m      7\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattempt_load_one_weight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattempt_load_weights\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparse_model\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myaml_model_load\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mguess_model_task\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      8\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mguess_model_scale\u001b[39m\u001b[38;5;124m'\u001b[39m, 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\u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodules\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AIFI, C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x,\n\u001b[1;32m     11\u001b[0m                                     Classify, Concat, Conv, Conv2, ConvTranspose, Detect, DWConv, DWConvTranspose2d,\n\u001b[1;32m     12\u001b[0m                                     Focus, GhostBottleneck, GhostConv, HGBlock, HGStem, Pose, RepC3, RepConv,\n\u001b[1;32m     13\u001b[0m                                     RTDETRDecoder, Segment)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchecks\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_requirements, check_suffix, check_yaml\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/nn/modules/__init__.py:20\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblock\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (C1, C2, C3, C3TR, DFL, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, GhostBottleneck,\n\u001b[1;32m     17\u001b[0m                     HGBlock, HGStem, Proto, RepC3)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconv\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (CBAM, ChannelAttention, Concat, Conv, Conv2, ConvTranspose, DWConv, DWConvTranspose2d, Focus,\n\u001b[1;32m     19\u001b[0m                    GhostConv, LightConv, RepConv, SpatialAttention)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhead\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Classify, Detect, Pose, RTDETRDecoder, Segment\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransformer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AIFI, MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer, LayerNorm2d,\n\u001b[1;32m     22\u001b[0m                           MLPBlock, MSDeformAttn, TransformerBlock, TransformerEncoderLayer, TransformerLayer)\n\u001b[1;32m     24\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConv2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLightConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRepConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDWConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDWConvTranspose2d\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConvTranspose\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFocus\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     25\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGhostConv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChannelAttention\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpatialAttention\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCBAM\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConcat\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransformerLayer\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     26\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransformerBlock\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMLPBlock\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLayerNorm2d\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDFL\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHGBlock\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHGStem\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPP\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPPF\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     27\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC2f\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3x\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3TR\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC3Ghost\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGhostBottleneck\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBottleneck\u001b[39m\u001b[38;5;124m'\u001b[39m, 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------------------------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m    764\u001b[0m \n\u001b[1;32m    765\u001b[0m \u001b[38;5;66;03m# Check first-install steps\u001b[39;00m\n\u001b[1;32m    766\u001b[0m PREFIX \u001b[38;5;241m=\u001b[39m colorstr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUltralytics: \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 767\u001b[0m SETTINGS \u001b[38;5;241m=\u001b[39m \u001b[43mget_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    768\u001b[0m DATASETS_DIR \u001b[38;5;241m=\u001b[39m Path(SETTINGS[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatasets_dir\u001b[39m\u001b[38;5;124m'\u001b[39m])  \u001b[38;5;66;03m# global datasets directory\u001b[39;00m\n\u001b[1;32m    769\u001b[0m ENVIRONMENT \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mColab\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_colab() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKaggle\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_kaggle() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJupyter\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_jupyter() \u001b[38;5;28;01melse\u001b[39;00m \\\n\u001b[1;32m    770\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDocker\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_docker() \u001b[38;5;28;01melse\u001b[39;00m platform\u001b[38;5;241m.\u001b[39msystem()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/utils/__init__.py:700\u001b[0m, in \u001b[0;36mget_settings\u001b[0;34m(file, version)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mhashlib\u001b[39;00m\n\u001b[1;32m    699\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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\u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/utils/torch_utils.py:18\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctional\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mF\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      3\u001b[0m     CarlaStereo,\n\u001b[1;32m      4\u001b[0m     CREStereo,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m     SintelStereo,\n\u001b[1;32m     13\u001b[0m )\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcaltech\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Caltech101, Caltech256\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/datasets/_optical_flow.py:12\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _read_png_16\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _read_pfm, verify_str_arg\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VisionDataset\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/io/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, Iterator\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _log_api_usage_once\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_load_gpu_decoder\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _HAS_GPU_VIDEO_DECODER\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/utils.py:11\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageColor, ImageDraw, ImageFont\n\u001b[1;32m     13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmake_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msave_image\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflow_to_image\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     20\u001b[0m ]\n\u001b[1;32m     23\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mno_grad()\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_grid\u001b[39m(\n\u001b[1;32m     25\u001b[0m     tensor: Union[torch\u001b[38;5;241m.\u001b[39mTensor, List[torch\u001b[38;5;241m.\u001b[39mTensor]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m     33\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/PIL/ImageDraw.py:36\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumbers\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageColor, ImageFont\n\u001b[1;32m     38\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;124;03mA simple 2D drawing interface for PIL images.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;124;03m<p>\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;124;03mApplication code should use the <b>Draw</b> factory, instead of\u001b[39;00m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;124;03mdirectly.\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mImageDraw\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/PIL/ImageFont.py:34\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BytesIO\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_util\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m isDirectory, isPath\n\u001b[1;32m     36\u001b[0m LAYOUT_BASIC \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     37\u001b[0m LAYOUT_RAQM \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n","\u001b[0;31mImportError\u001b[0m: cannot import name 'isDirectory' from 'PIL._util' (/opt/conda/lib/python3.10/site-packages/PIL/_util.py)"],"ename":"ImportError","evalue":"cannot import name 'isDirectory' from 'PIL._util' (/opt/conda/lib/python3.10/site-packages/PIL/_util.py)","output_type":"error"}]},{"cell_type":"code","source":"project.version(dataset.version).deploy(model_type=\"yolov8\", model_path=f\"/kaggle/working/runs/segment/train/weights\")","metadata":{"execution":{"iopub.status.busy":"2023-08-02T17:31:05.664368Z","iopub.execute_input":"2023-08-02T17:31:05.664758Z","iopub.status.idle":"2023-08-02T17:31:07.688015Z","shell.execute_reply.started":"2023-08-02T17:31:05.664722Z","shell.execute_reply":"2023-08-02T17:31:07.686198Z"},"trusted":true},"execution_count":26,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/roboflow/core/version.py:459\u001b[0m, in \u001b[0;36mVersion.deploy\u001b[0;34m(self, model_type, model_path)\u001b[0m\n\u001b[1;32m    458\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m--> 459\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/__init__.py:5\u001b[0m\n\u001b[1;32m      3\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m8.0.134\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m start\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrtdetr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RTDETR\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/hub/__init__.py:5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauth\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Auth\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PREFIX\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/hub/auth.py:5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HUB_API_ROOT, PREFIX, request_with_credentials\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LOGGER, SETTINGS, emojis, is_colab, set_settings\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/hub/utils.py:14\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM_BAR_FORMAT,\n\u001b[1;32m     15\u001b[0m                                     TryExcept, __version__, colorstr, get_git_origin_url, is_colab, is_git_dir,\n\u001b[1;32m     16\u001b[0m                                     is_pip_package)\n\u001b[1;32m     18\u001b[0m PREFIX \u001b[38;5;241m=\u001b[39m colorstr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUltralytics HUB: \u001b[39m\u001b[38;5;124m'\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v8\n\u001b[1;32m      5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mv8\u001b[39m\u001b[38;5;124m'\u001b[39m,  \u001b[38;5;66;03m# tuple or list\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m classify, detect, pose, segment\n\u001b[1;32m      5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclassify\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msegment\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdetect\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpose\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/v8/classify/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv8\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredict\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationPredictor, predict\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattempt_load_one_weight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattempt_load_weights\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparse_model\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myaml_model_load\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mguess_model_task\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      8\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mguess_model_scale\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtorch_safe_load\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDetectionModel\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSegmentationModel\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClassificationModel\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      9\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBaseModel\u001b[39m\u001b[38;5;124m'\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/nn/tasks.py:10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodules\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AIFI, C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x,\n\u001b[1;32m     11\u001b[0m                                     Classify, Concat, Conv, Conv2, ConvTranspose, Detect, DWConv, DWConvTranspose2d,\n\u001b[1;32m     12\u001b[0m                                     Focus, GhostBottleneck, GhostConv, HGBlock, HGStem, Pose, RepC3, RepConv,\n\u001b[1;32m     13\u001b[0m                                     RTDETRDecoder, Segment)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/nn/modules/__init__.py:20\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconv\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (CBAM, ChannelAttention, Concat, Conv, Conv2, ConvTranspose, DWConv, DWConvTranspose2d, Focus,\n\u001b[1;32m     19\u001b[0m                    GhostConv, LightConv, RepConv, SpatialAttention)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhead\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Classify, Detect, Pose, RTDETRDecoder, Segment\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransformer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AIFI, MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer, LayerNorm2d,\n\u001b[1;32m     22\u001b[0m                           MLPBlock, MSDeformAttn, TransformerBlock, TransformerEncoderLayer, TransformerLayer)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/nn/modules/head.py:12\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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colorstr(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUltralytics: \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 767\u001b[0m SETTINGS \u001b[38;5;241m=\u001b[39m \u001b[43mget_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    768\u001b[0m DATASETS_DIR \u001b[38;5;241m=\u001b[39m Path(SETTINGS[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatasets_dir\u001b[39m\u001b[38;5;124m'\u001b[39m])  \u001b[38;5;66;03m# global datasets directory\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/yolo/utils/__init__.py:700\u001b[0m, in \u001b[0;36mget_settings\u001b[0;34m(file, version)\u001b[0m\n\u001b[1;32m    699\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01myolo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchecks\u001b[39;00m 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_HAS_OPS\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/datasets/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_optical_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_stereo_matching\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      3\u001b[0m     CarlaStereo,\n\u001b[1;32m      4\u001b[0m     CREStereo,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m     SintelStereo,\n\u001b[1;32m     13\u001b[0m )\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/datasets/_optical_flow.py:12\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _read_png_16\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _read_pfm, verify_str_arg\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/io/__init__.py:5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _log_api_usage_once\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torchvision/utils.py:11\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageColor, ImageDraw, ImageFont\n\u001b[1;32m     13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmake_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msave_image\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflow_to_image\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     20\u001b[0m ]\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/PIL/ImageDraw.py:36\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumbers\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageColor, ImageFont\n\u001b[1;32m     38\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;124;03mA simple 2D drawing interface for PIL images.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;124;03m<p>\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;124;03mApplication code should use the <b>Draw</b> factory, instead of\u001b[39;00m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;124;03mdirectly.\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/PIL/ImageFont.py:34\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_util\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m isDirectory, isPath\n\u001b[1;32m     36\u001b[0m LAYOUT_BASIC \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n","\u001b[0;31mImportError\u001b[0m: cannot import name 'isDirectory' from 'PIL._util' (/opt/conda/lib/python3.10/site-packages/PIL/_util.py)","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mproject\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mversion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43myolov8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/kaggle/working/runs/segment/train/weights\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/roboflow/core/version.py:462\u001b[0m, in \u001b[0;36mVersion.deploy\u001b[0;34m(self, model_type, model_path)\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\n\u001b[1;32m    461\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 462\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m (\n\u001b[1;32m    463\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe ultralytics python package is required to deploy yolov8 models. Please install it with `pip install ultralytics`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    464\u001b[0m         )\n\u001b[1;32m    466\u001b[0m     print_warn_for_wrong_dependencies_versions(\n\u001b[1;32m    467\u001b[0m         [(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124multralytics\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m==\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m8.0.134\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n\u001b[1;32m    468\u001b[0m     )\n\u001b[1;32m    470\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myolov5\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m model_type \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myolov7\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m model_type:\n","\u001b[0;31mTypeError\u001b[0m: exceptions must derive from BaseException"],"ename":"TypeError","evalue":"exceptions must derive from BaseException","output_type":"error"}]}]}