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@AIWintermuteAI
AIWintermuteAI committed Dec 14, 2021
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84 changes: 45 additions & 39 deletions resources/aXeleRate_pascal20_detector.ipynb
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"provenance": [],
"collapsed_sections": [],
"mount_file_id": "1_yhmzOZKns_-h0GwyPu9YAT3K0WQ1PG8",
"authorship_tag": "ABX9TyNngjnxUG9lnl+vJGB5gnHo",
"authorship_tag": "ABX9TyPUzrsszS4m23mnB7AcN0I9",
"include_colab_link": true
},
"kernelspec": {
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"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/AIWintermuteAI/aXeleRate/blob/dev/resources/aXeleRate_pascal20_detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/AIWintermuteAI/aXeleRate/blob/master/resources/aXeleRate_pascal20_detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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"!git clone https://github.com/AIWintermuteAI/aXeleRate.git\n",
"import sys\n",
"sys.path.append('/content/aXeleRate')\n",
"from axelerate import setup_training, setup_inference"
"from axelerate import setup_training, setup_inference, setup_evaluation"
],
"execution_count": null,
"outputs": []
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"source": [
"%matplotlib inline\n",
"!gdown https://drive.google.com/uc?id=1xgk7svdjBiEyzyUVoZrCz4PP6dSjVL8S #pascal-voc dataset\n",
"!gdown https://drive.google.com/uc?id=1-ccBXBEUhyzG2_jopf6d13hTH9X7qpNl #pre-trained model\n",
"!gdown https://drive.google.com/uc?id=1-2jYfTRPX4kSUTL5SUQVxwHKjBclrBTA #pre-trained model\n",
"!unzip --qq pascal_20_detection.zip\n",
"\n",
"from axelerate.networks.common_utils.augment import visualize_detection_dataset\n",
"\n",
"visualize_detection_dataset(img_folder='pascal_20_detection/imgs_validation', ann_folder='pascal_20_detection/anns_validation', num_imgs=10, img_size=224, augment=True)"
"visualize_detection_dataset(img_folder='pascal_20_detection/imgs_validation', ann_folder='pascal_20_detection/anns_validation', num_imgs=10, img_size=320, augment=True)\n"
],
"execution_count": null,
"outputs": []
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"- DenseNet121\n",
"- ResNet50\n",
"\n",
"Currently only MobileNet backends available for YOLOv3 detector. I'm working on backend (feature exctractor) overhaul.\n",
"\n",
"For more information on anchors, please read here\n",
"https://github.com/pjreddie/darknet/issues/568\n",
"\n",
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"\n",
"coord_scale determines how much to penalize wrong position and size predictions (x, y, w, h)\n",
"\n",
"class_scale determines how much to penalize wrong class prediction\n",
"obj_thresh, nms_threshold set detection confidence threshold and nms thresholds to be used when calcualting precision/recall\n",
"\n",
"For converter type you can choose the following:\n",
"\n",
"'k210', 'tflite_fullint', 'tflite_dynamic', 'edgetpu', 'openvino', 'onnx'\n",
"\n",
"**Since it is an example notebook, we will use pretrained weights and set all layers of the model to be \"frozen\"(non-trainable).** "
"**Since it is an example notebook, we will use pretrained weights and set learning rate to 0.0** "
]
},
{
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" \"model\":{\n",
" \"type\": \"Detector\",\n",
" \"architecture\": \"MobileNet1_0\",\n",
" \"input_size\": 224,\n",
" \"anchors\": [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828],\n",
" \"input_size\": [224, 320],\n",
" \"anchors\": [[[0.76120044, 0.57155991], [0.6923348, 0.88535553], [0.47163042, 0.34163313]],\n",
" [[0.33340788, 0.70065861], [0.18124964, 0.38986752], [0.08497349, 0.1527057 ]]],\n",
" \"labels\": [\"person\", \"bird\", \"cat\", \"cow\", \"dog\", \"horse\", \"sheep\", \"aeroplane\", \"bicycle\", \"boat\", \"bus\", \"car\", \"motorbike\", \"train\",\"bottle\", \"chair\", \"diningtable\", \"pottedplant\", \"sofa\", \"tvmonitor\"],\n",
" \"coord_scale\" : \t\t1.0,\n",
" \"class_scale\" : \t\t1.0,\n",
" \"object_scale\" : \t\t5.0,\n",
" \"obj_thresh\" : \t\t 0.7,\n",
" \"iou_thresh\" : \t\t 0.5,\n",
" \"coord_scale\" : \t\t 1.0,\n",
" \"object_scale\" : \t\t 3.0, \n",
" \"no_object_scale\" : \t1.0\n",
" },\n",
" \"weights\" : {\n",
" \"full\": \t\t\t\t\"/content/2020-04-12_17-09-43.h5\",\n",
" \"full\": \t\t\t\t \"/content/yolo_best_recall.h5\",\n",
" \"backend\": \t\t \"imagenet\"\n",
" },\n",
" \"train\" : {\n",
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" \"valid_image_folder\": \"pascal_20_detection/imgs_validation\",\n",
" \"valid_annot_folder\": \"pascal_20_detection/anns_validation\",\n",
" \"valid_times\": 1,\n",
" \"valid_metric\": \"mAP\",\n",
" \"valid_metric\": \"recall\",\n",
" \"batch_size\": 32,\n",
" \"learning_rate\": 0.0,\n",
" \"saved_folder\": \t\tF\"/content/drive/MyDrive/pascal20_detection\",\n",
" \"first_trainable_layer\": \"reshape\",\n",
" \"augumentation\":\t\t\t\tFalse,\n",
" \"is_only_detect\" : \t\tFalse\n",
" \"saved_folder\": \t\tF\"/content/drive/MyDrive/projects/pascal20_yolov3\",\n",
" \"first_trainable_layer\": \"\",\n",
" \"augmentation\":\t\t\t\t True,\n",
" \"is_only_detect\" : \t\t False\n",
" },\n",
" \"converter\" : {\n",
" \"type\": \t\t\t\t[\"k210\"]\n",
" \"type\": \t\t\t\t[]\n",
" }\n",
" }"
"}"
],
"execution_count": null,
"outputs": []
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"id": "i0Fc61WrTxh1"
},
"source": [
"Also, let's open Tensorboard, where we will be able to watch model training progress in real time. Training and validation logs also will be saved in project folder.\r\n",
"Also, let's open Tensorboard, where we will be able to watch model training progress in real time. Training and validation logs also will be saved in project folder.\n",
"Since there are no logs before we start the training, tensorboard will be empty. Refresh it after first epoch."
]
},
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"id": "jsGp9JvjTzzp"
},
"source": [
"%load_ext tensorboard\r\n",
"%tensorboard --logdir logs\r\n",
"!sleep 10"
"%load_ext tensorboard\n",
"%tensorboard --logdir logs\n",
"!sleep 5"
],
"execution_count": null,
"outputs": []
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},
"source": [
"from axelerate.networks.common_utils.convert import Converter\n",
"converter = Converter('openvino', 'MobileNet1_0', 'pascal_20_detection/imgs_validation')\n",
"converter = Converter('tflite_dynamic', 'MobileNet1_0', 'pascal_20_detection/imgs_validation')\n",
"converter.convert_model(model_path)"
],
"execution_count": null,
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" \"model\":{\n",
" \"type\": \"Detector\",\n",
" \"architecture\": \"MobileNet1_0\",\n",
" \"input_size\": 224,\n",
" \"anchors\": [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828],\n",
" \"input_size\": [224, 320],\n",
" \"anchors\": [[[0.76120044, 0.57155991], [0.6923348, 0.88535553], [0.47163042, 0.34163313]],\n",
" [[0.33340788, 0.70065861], [0.18124964, 0.38986752], [0.08497349, 0.1527057 ]]],\n",
" \"labels\": [\"person\", \"bird\", \"cat\", \"cow\", \"dog\", \"horse\", \"sheep\", \"aeroplane\", \"bicycle\", \"boat\", \"bus\", \"car\", \"motorbike\", \"train\",\"bottle\", \"chair\", \"diningtable\", \"pottedplant\", \"sofa\", \"tvmonitor\"],\n",
" \"coord_scale\" : \t\t1.0,\n",
" \"class_scale\" : \t\t1.0,\n",
" \"object_scale\" : \t\t5.0,\n",
" \"obj_thresh\" : \t\t 0.7,\n",
" \"iou_thresh\" : \t\t 0.5,\n",
" \"coord_scale\" : \t\t 1.0,\n",
" \"object_scale\" : \t\t 3.0, \n",
" \"no_object_scale\" : \t1.0\n",
" },\n",
" \"weights\" : {\n",
" \"full\": \t\t\t\t\"\",\n",
" \"full\": \t\t\t\t \"\",\n",
" \"backend\": \t\t \"imagenet\"\n",
" },\n",
" \"train\" : {\n",
" \"actual_epoch\": 100,\n",
" \"actual_epoch\": 50,\n",
" \"train_image_folder\": \"pascal_20_detection/imgs\",\n",
" \"train_annot_folder\": \"pascal_20_detection/anns\",\n",
" \"train_times\": 1,\n",
" \"valid_image_folder\": \"pascal_20_detection/imgs_validation\",\n",
" \"valid_annot_folder\": \"pascal_20_detection/anns_validation\",\n",
" \"valid_times\": 1,\n",
" \"valid_metric\": \"mAP\",\n",
" \"valid_metric\": \"recall\",\n",
" \"batch_size\": 32,\n",
" \"learning_rate\": 1e-4,\n",
" \"saved_folder\": \t\tF\"/content/drive/MyDrive/pascal20_detection\",\n",
" \"learning_rate\": 1e-3,\n",
" \"saved_folder\": \t\tF\"/content/drive/MyDrive/projects/pascal20_yolov3\",\n",
" \"first_trainable_layer\": \"\",\n",
" \"augumentation\":\t\t\t\tFalse,\n",
" \"is_only_detect\" : \t\tFalse\n",
" \"augmentation\":\t\t\t\t True,\n",
" \"is_only_detect\" : \t\t False\n",
" },\n",
" \"converter\" : {\n",
" \"type\": \t\t\t\t[\"k210\",\"tflite\"]\n",
" \"type\": \t\t\t\t[]\n",
" }\n",
" }"
"}"
],
"execution_count": null,
"outputs": []
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