datasets/pose/hand-keypoints/

[giscus[bot]]

datasets/pose/hand-keypoints/

Explore the hand keypoints estimation dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.

https://docs.ultralytics.com/datasets/pose/hand-keypoints/

[redsuning]

train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0379]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001796.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0006]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0027]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0049]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0051 1.0038 1.0088 1.0114]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0048 1.0062 1.0099 1.0121]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001888.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1 1.0017]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001994.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0059]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0044]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00001999.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.007]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002000.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0083]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0178]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0178]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002143.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0582 1.0216]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002144.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0641 1.0333]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0041 1.0052 1.008 1.0093 1.0108 1.0112 1.0116]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0121]
train: WARNING ⚠️ G:\datasets\hand\hand-keypoints\train\images\IMG_00002454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.023 1.0012]
Many of the callout coordinates are beyond 1

[glenn-jocher]

Thank you for bringing this to our attention. We'll review the dataset annotations for any inconsistencies. If you have further questions, feel free to ask here.

[AlgosDuCouteau]

seem like the issue has not been fixed, i am still get the errors, will it affect training results?

[glenn-jocher]

The warnings won't affect training results if the majority of your dataset is intact. We'll review the dataset annotations for any issues. For further details, please refer to the hand-keypoints documentation.

[berlintop]

In fact, the official dataset has 5% incorrect labels.

[glenn-jocher]

Thank you for pointing this out. We will investigate and address any labeling issues in the Hand Keypoints dataset as soon as possible. Meanwhile, you may exclude problematic samples or adjust the annotations for your use case to ensure optimal training results.

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! It looks like you're exploring hand keypoints estimation datasets for advanced pose estimation. You can find detailed information and resources in our documentation.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. An Ultralytics engineer will also assist you soon.

For custom training ❓ questions, please share your dataset, any relevant training logs, and ensure you're following our Tips for Best Training Results.

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[chengziNaomi]

In yolov8-pose, what effect does the number of key points have on the detection results? Less key points, better results or more key points, better results? Now I want to identify the basket in the field, do I identify the four corner points on the top of the basket or the eight corner points on the basket?

[glenn-jocher]

@chengziNaomi the number of keypoints can affect detection results based on your specific application. For identifying a basket, consider the complexity and precision needed. Using more keypoints might provide better detail, but it depends on your use case. For further guidance, you can explore the hand keypoints documentation at Ultralytics Docs.

[chengziNaomi]

Thank you for your reply. I have one more question. Why is the ratio of train and valid about 2:1 in this hand
keypoints datasets? but in seg, the ratio is 10:1:1？Is it a special requirements from yolov8-pose？

[glenn-jocher]

The dataset split ratios are often determined by the specific requirements of the task and dataset characteristics. For hand keypoints, a 2:1 ratio might balance training and validation needs effectively, while segmentation tasks might use a 10:1:1 ratio to ensure sufficient data for training, validation, and testing. These choices are not specific to YOLOv8-pose but are tailored to optimize model performance for each dataset type.

[chengziNaomi]

Thank you very much! I want to add negative samples to the datasets, what is the way for yolov8-pose to add negative samples?All I need to do is add the background image and blank txt text to train and val，right？

[glenn-jocher]

To add negative samples in YOLOv8-pose, you can include background images with corresponding blank .txt files in your dataset. This approach helps the model learn to distinguish between relevant and irrelevant features.

[chrismuntean]

I trained a model using this tutorial so you don't have to!

Feel free to use it as you wish:

https://github.com/chrismuntean/YOLO11n-pose-hands

[glenn-jocher]

@chrismuntean thank you for sharing your model! It's great to see community contributions that enhance the use of YOLO11 for pose estimation tasks.

[berlintop]

In fact, the official dataset has 5% incorrect labels.

[glenn-jocher]

Thank you for pointing that out. If the dataset contains mislabeled annotations, you may need to inspect and correct them before training to ensure optimal performance. For further details, you can review the dataset configuration and annotations provided in the hand-keypoints.yaml. Let us know if you encounter any specific issues during training.

[jugal-sheth]

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640) 

Error

train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035498.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035499.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035500.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
.
.
.
.
.
.
.
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035501.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035503.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035504.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035505.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035507.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
train: WARNING ⚠️ D:\projects\hand-track\datasets\dataset\train\images\IMG_00035509.jpg: ignoring corrupt image/label: cannot reshape array of size 67 into shape (2)
WARNING ⚠️ No images found in D:\projects\hand-track\datasets\dataset\train\labels.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formattingg guidance.
Traceback (most recent call last):
File "d:\projects\hand-track\test.py", line 36, in
model.train(data="config.yaml", epochs=100, imgsz=896, device=0)
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\engine\model.py", line 808, in train
self.trainer.train()
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\engine\trainer.py", line 207, in train
self._do_train(world_size)
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\engine\trainer.py", line 322, in _do_train
self._setup_train(world_size)
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\engine\trainer.py", line 286, in _setup_train
self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=LOCAL_RANK, mode="train")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\models\yolo\detect\train.py", line 49, in get_dataloader
dataset = self.build_dataset(dataset_path, mode, batch_size)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\models\yolo\detect\train.py", line 43, in build_dataset
return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\data\build.py", line 99, in build_yolo_dataset
return dataset(
^^^^^^^^
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\data\dataset.py", line 64, in init
super().init(*args, **kwargs)
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\data\base.py", line 74, in init
self.labels = self.get_labels()
^^^^^^^^^^^^^^^^^
File "C:\Users\jugal\miniconda3\envs\hand\Lib\site-packages\ultralytics\data\dataset.py", line 161, in get_labels
len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: not enough values to unpack (expected 3, got 0)

[glenn-jocher]

@jugal-sheth hi there! It looks like your labels might be incorrectly formatted for hand keypoints estimation. The hand-keypoints dataset requires 21 keypoints with 3 dimensions each (x,y,visibility), but your labels appear to have 67 values instead of the required 63 (21*3). Please verify your label files match the required format shown in our hand-keypoints documentation: https://docs.ultralytics.com/datasets/pose/hand-keypoints/#dataset-yaml 💡

For commercial licensing options, visit https://ultralytics.com/license. Let us know if you need any other assistance!

[jugal-sheth]

Hi @glenn-jocher Thank you for your reply
I haven't changed anything in sample code given on this website
https://docs.ultralytics.com/datasets/pose/hand-keypoints/#how-do-i-train-a-yolo11-model-on-the-hand-keypoints-dataset

[glenn-jocher]

@jugal-sheth hi there! It seems your custom labels may not include the required bounding box coordinates alongside keypoints. The YOLO pose format requires each label to start with class, x_center, y_center, width, height (5 values) followed by 21 keypoints (63 values), totaling 68 values per label. Your 67-value labels suggest missing one element. Please verify your label format matches our pose estimation requirements exactly. Let us know if you need further clarification! 🚀