# 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?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?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**
task_categories:image-classification language:en license:cc-by-nc-nd-4.0 code finance region:us
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification"], "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}}
TAGS
#task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #code #finance #region-us 

# 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 URL to discuss your requirements, learn about the price and buy the dataset.

![](URL

# Data Format

Each image from 'EP' folder is accompanied by an XML-annotation in the 'URL' 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
![](URL

# 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 provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: URL

TrainingData's GitHub: URL
# 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 URL to discuss your requirements, learn about the price and buy the dataset.

![](URL # Data Format

Each image from 'EP' folder is accompanied by an XML-annotation in the 'URL' 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
![](URL # 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 provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: URL

TrainingData's GitHub: URL
TAGS
#task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #code #finance #region-us 
 # 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 URL to discuss your requirements, learn about the price and buy the dataset.

![](URL # Data Format

Each image from 'EP' folder is accompanied by an XML-annotation in the 'URL' 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
![](URL # 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 provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: URL

TrainingData's GitHub: URL