--- license: cc-by-nc-nd-4.0 task_categories: - image-classification language: - en tags: - code - finance dataset_info: features: - name: image_id dtype: uint32 - name: image dtype: image - name: mask dtype: image - name: shapes dtype: string splits: - name: train num_bytes: 142645152 num_examples: 29 download_size: 137240523 dataset_size: 142645152 --- # Pose Estimation The dataset is primarly intended to dentify and predict the positions of major joints of a human body in an image. It consists of people's photographs with body part labeled with keypoints. # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/pose-estimation-annotation?utm_source=huggingface&utm_medium=cpc&utm_campaign=pose_estimation) to discuss your requirements, learn about the price and buy the dataset. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F31b38dee8dc63c581004afcf82136116%2F12.jpg?generation=1684357817470094&alt=media) # Data Format Each image from `EP` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the key points. For each point, the x and y coordinates are provided, and there is a `Presumed_Location` attribute, indicating whether the point is presumed or accurately defined. # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fc8b7cc938539368c9ec03dd01a26724c%2Fcarbon%20(1).png?generation=1684358333663868&alt=media) # Labeled body parts Each keypoint is ordered and corresponds to the concrete part of the body: 0. **Nose** 1. **Neck** 2. **Right shoulder** 3. **Right elbow** 4. **Right wrist** 5. **Left shoulder** 6. **Left elbow** 7. **Left wrist** 8. **Right hip** 9. **Right knee** 10. **Right foot** 11. **Left hip** 12. **Left knee** 13. **Left foot** 14. **Right eye** 15. **Left eye** 16. **Right ear** 17. **Left ear** # Keypoint annotation is made in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market/pose-estimation-annotation?utm_source=huggingface&utm_medium=cpc&utm_campaign=pose_estimation) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**