@xcf1 thank you for your question. The AssertionError typically occurs when your results file doesn't match COCO's expected format for keypoints. For COCO test-dev2017 submissions, please ensure your JSON follows the exact format shown in the COCO keypoints evaluation format. The keypoints array should contain 17 keypoints in (x,y,visibility) format where visibility=2 for visible points.

Here's a minimal validation script to generate correct JSON:

from ultralytics import YOLO

model = YOLO('yolo26n-pose.pt')
model.val(data='coco-pose.yaml', split='test-dev', save_json=True)

This will automatically generate a properly formatted predictions.json in your runs/val directory. For further assistance, please review the COCO evaluation guidelines.

Thank you very much for your reply. I have obtained the results.json file, but when I upload its compressed package to the COCO official website, the webpage displays a file loading failure. What could be the reason for this? My compressed file size is 238MB, while the COCO official website specifies a size of about 200MB. Also, the img_id in this JSON file is arranged in descending order. Is there a requirement for the order of IDs? I have also noticed an issue: for the image with img_id 16, the corresponding generated txt file contains many lines of data, even though there are only two people in the image, suggesting a mismatch in data. Other images seem to be normal.

this is isuue_img

Thank you for the follow-up. The file size discrepancy and ID ordering issues typically occur when not using the official test-dev2017 split. For COCO evaluation server submissions, please ensure you're validating with split='test-dev' in your validation command to generate results that strictly correspond to the 20,288 test images. The JSON should auto-generate with correct IDs and sizing when using:

model.val(data='coco-pose.yaml', split='test-dev', save_json=True)

Validation already uses a low confidence threshold by default (conf=0.001), so you normally don't need to set conf. If issues persist, please verify your dataset YAML points to test-dev2017.txt as shown in the COCO-Pose dataset configuration.

Hello, thank you for your reply. I have increased the conf value to reduce the detection size, and now it can be uploaded to the COCO official website for testing. However, I still get the error “AssertionError: Results do not correspond to current coco set.” The content of the JSON file generated by the code you provided is as shown in the image below. What could be the reason for this? I have been struggling with this issue for a long time, and it still persists.

The "AssertionError" typically indicates mismatched image IDs between your results and COCO's test-dev2017 set. Please ensure you're validating with the exact command:
model.val(data='coco-pose.yaml', split='test-dev', save_json=True)
This guarantees proper ID alignment. For direct verification, compare your JSON's image IDs with those in the test-dev2017.txt from our dataset YAML.

Hello, I have already ensured that my results correspond to the images in the COCO2017 test-dev dataset. When I submitted a small subset of my model's inference results in JSON format to the COCO official service platform, it worked and I received evaluation results. However, after adding more images' JSON results, the platform threw an error: "AssertionError: Results do not correspond to current COCO set". I have confirmed that the additional images, such as the one with ID 426923, are indeed in the COCO2017 test-dev dataset. Could you please tell me why this is happening? Thank you very much. hank you very much.

The AssertionError typically occurs when your JSON contains image IDs not present in the official COCO test-dev2017 split (20,288 images). Please verify that all image IDs in your results match those in the test-dev2017.txt list from our dataset YAML. For validation, try loading your JSON with the COCO API's loadRes() function to detect mismatches.

thank you very much，the isuue is solved

Great to hear your issue is resolved! For future users encountering similar COCO evaluation challenges, ensure your dataset YAML points to the correct test split and use the official model.val() method to generate results. Let us know if any other questions arise.

@xcf1 ensure your results JSON file uses image IDs from the COCO test-dev2017 set (20,288 images) and follows the exact keypoints format required. The error often occurs when using validation set IDs or incorrect keypoint ordering. Double-check against the COCO Keypoints Evaluation page submission requirements.

@LeBron-Jian using a pretrained model like YOLOv8n-pose provides a strong starting point, allowing you to fine-tune on your specific data to potentially improve accuracy. To reproduce the effects on the COCO dataset, ensure you're using the correct dataset configuration and training parameters as outlined in the coco-pose.yaml. For more details, refer to the COCO-Pose documentation.

@glenn-jocher hi, thanks for your reply. I went through the documentation https://docs.ultralytics.com/datasets/pose/coco/,。 I used the original configuration file and did not use the pre-trained model. Can this reproduce the effect of yolo-pose on the coco dataset? the code is as follows:

    #  build a new model from YAML
    model = YOLO("cfg/models/v8/yolov8-pose.yaml")  
    # Train the model
    model.train(data='cfg/datasets/coco-pose.yaml', batch=16, workers=8, device=[0, 1])

To reproduce the YOLO-Pose results on the COCO dataset, ensure you're using the correct coco-pose.yaml configuration and consider starting with a pretrained model for better initial performance. Fine-tuning on your specific data can help improve accuracy.

To reproduce the YOLO-Pose results on the COCO dataset, ensure you're using the correct coco-pose.yaml configuration and consider starting with a pretrained model for better initial performance. Fine-tuning on your specific data can help improve accuracy.

hi， I trained the pose model on my own small dataset based on the pre-trained model. my training set is about 200 pictures and the validation set is 40 pictures. the scene is a very simple scene with no occlusion and pure background. I found that the mAP₅₀~95 of both bbox and keypoint are very high, but the keypoints of the actual test pictures are not so accurate. why is this? the only thing i can think of is the setting of sigmas used in the calculation of metrics. the sigmas i gave is the default torch.ones(nkpt)/nkpt in the code

High mAP but inaccurate keypoints can result from overfitting, especially with a small dataset. Consider increasing your dataset size or using data augmentation to improve generalization. Adjusting sigma values might help, but focus on data diversity first.

Hello, according to your suggestion, I diversified my own small hand dataset. Then I trained it based on the pre-trained model. I found a strange phenomenon, it is not that the larger the model, the better the effect. (Based on the experience of object detection, the X model is always better than the L model, M model, and S model), but the mAP of the bbox here is relatively stable. As for the mAP of Pose, the mAP of the X model reached 0.888, but the L model only had 0.664, the M model had 0.748, the S model had 0.928, and the nano model had an accuracy of 0.914. This conclusion was obtained when I controlled the training set and the validation set, and epochs=100. This makes me a little confused, why is it? Does it mean that the fine-tuning of the pre-trained model for a small dataset is not that the larger the model, the better the effect?

Is it because the larger the model, the harder it is to converge? Do I need more epochs?

@LeBron-Jian model size doesn't always correlate with better performance, especially on small datasets. Smaller models might generalize better due to fewer parameters. Consider experimenting with different architectures and hyperparameters to find the best fit for your specific dataset.

@xhf3571 hello,

It looks like the error is due to a missing or incorrect path to the test-dev2017.txt file. Ensure that your dataset directory structure matches the paths specified in your coco-pose.yaml file. Verify that the test-dev2017.txt file exists in the correct location.

Regarding the output file format, your JSON should include the image ID, category ID, bounding box coordinates, confidence score, and keypoints. This format is suitable for submission to the COCO evaluation server, as specified in the competition guidelines.

For more details on dataset formatting, you can refer to the Ultralytics documentation.

Best of luck!

以下是英文简洁版的提问： I adjusted and ran the code, but it still reports an error saying no labels found. I think it is normal for the test set to have no labels. The error is as follows:

WARNING ⚠️ No labels found in /mnt/workspace/ultralytics-8.1.45/ultralytics/assets/test2017.cache, training may not work correctly. See https://docs.ultralytics.com/datasets/detect for dataset formatting guidance.
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 1268/1268 [00:57<00:00, 21.92it/s]
                   all      20288          0          0          0          0          0          0          0          0          0
WARNING ⚠️ no labels found in pose set, can not compute metrics without labels
Speed: 0.1ms preprocess, 1.3ms inference, 0.0ms loss, 0.6ms postprocess per image
Results saved to runs/pose/val6
Traceback (most recent call last):
  File "/mnt/workspace/ultralytics-8.1.45/ultralytics/coco-test.py", line 12, in <module>
    for result in results:
TypeError: 'PoseMetrics' object is not iterable

Thank you!

Hi there, I've looked through other responses and tried the following command:

```bash
(yolov8Pose) root@dsw-405310-8475c8cfcb-zbvfs:/mnt/workspace/ultralytics-8.1.45/ultralytics# python detect --source /mnt/workspace/ultralytics-8.1.45/ultralytics/assets/test2017 --weights /mnt/workspace/ultralytics-8.1.45/ultralytics/runs/train/C2F_CCA/weights/best.pt --save-json

However, I encountered an error:

Could you please advise which directory I should be running this command from?

Additionally, I tried running the following command:

python predict model=/mnt/workspace/ultralytics-8.1.45/ultralytics/runs/train/C2F_CCA/weights/best.pt data=coco-pose.yaml split=test

But I received the same type of error:

Could you help clarify where these scripts are located or if there is a step I might be missing?

Thanks for your help!

If the operation is successful, should a JSON file be generated? I noticed that the final submission to the website https://competitions.codalab.org/competitions/12061#participate is a ZIP file. Could you please let me know what else should be included in this ZIP file?

@xhf3571 yes, a JSON file should be generated if the operation is successful. For the final submission to the competition, you need to include this JSON file in a ZIP archive. The ZIP file should contain the JSON results formatted according to the competition guidelines. Make sure to follow the specific instructions provided on the competition's submission page to ensure your results are correctly evaluated.

I've posted my specific issue on the GitHub project, and the issue link is: https://github.com/ultralytics/ultralytics/issues/14510. I really need your help as I've been stuck for 3 days now. Thank you so much!

@xhf3571 thank you for sharing the issue link. Please ensure you are using the latest version of the Ultralytics package, as updates often resolve such issues. The error indicates that no labels were found, which is expected for the test set. The PoseMetrics object is not iterable because model.val() returns a metrics object, not a list of results.

To generate the COCO-format JSON for the test set, set save_json=True:

from ultralytics import YOLO

# Load the model
model = YOLO("/my/path/best.pt")

# Validate on the test split and save COCO-format predictions
metrics = model.val(data="coco-pose.yaml", split="test", save_json=True)

Ensure the paths in your coco-pose.yaml are correct and point to the appropriate test set files. A predictions.json file is written to the validation run directory (for example runs/pose/val); zip it and submit it as required by the competition guidelines. If the issue persists, please update the GitHub issue with any new error messages or behavior observed.

Thank you for your response. I have now switched to the latest version of YOLOv8, version 8.2.59, and am running the following code:

```python
from ultralytics import YOLO
import json

# Load the model
model = YOLO("/root/lanyun-tmp/ultralytics-8.2.59/ultralytics/runs/train/C2F_CCA/weights/best.pt")

# Run inference on the test set
results = model.predict(source="/root/lanyun-tmp/ultralytics-8.1.45/ultralytics/assets/test2017")

# Convert results to COCO format
coco_results = []
for result in results:
    for box in result.boxes:
        coco_results.append({
            "image_id": result.image_id,
            "category_id": box.cls,
            "bbox": box.xywh.tolist(),
            "score": box.conf,
            "keypoints": box.keypoints.tolist()
        })

# Save results to JSON file
with open("coco_results.json", "w") as f:
    json.dump(coco_results, f)

I encountered the following error:

image 40665/40670 /root/lanyun-tmp/ultralytics-8.1.45/ultralytics/assets/test2017/000000581864.jpg: 480x640 2 persons, 16.0ms
image 40666/40670 /root/lanyun-tmp/ultralytics-8.1.45/ultralytics/assets/test2017/000000581872.jpg: 480x640 (no detections), 15.5ms
image 40667/40670 /root/lanyun-tmp/ultralytics-8.1.45/ultralytics/assets/test2017/000000581897.jpg: 448x640 (no detections), 15.8ms
image 40668/40670 /root/lanyun-tmp/ultralytics-8.1.45/ultralytics/assets/test2017/000000581908.jpg: 640x640 (no detections), 18.1ms
image 40669/40670 Traceback (most recent call last):
  File "/root/autodl-tmp/ultralytics-8.2.59/ultralytics/coco-test.py", line 17, in <module>
    "image_id": result.image_id,
  File "/root/autodl-tmp/ultralytics-8.2.59/ultralytics/utils/__init__.py", line 160, in __getattr__
    raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
AttributeError: 'Results' object has no attribute 'image_id'. See valid attributes below.
ValueError: not enough values to unpack (expected 4, got 1)

I am using the new version of YOLOv8, and it seems to predict correctly, but I am still unable to generate the JSON file for evaluation on the COCO test set. Could you please provide me with the correct and complete code to generate this? I have been working on this for four days now.

Thank you and look forward to your reply

@xhf3571 thank you for updating to the latest version. The error you're encountering indicates that the Results object does not have an image_id attribute, and result.boxes does not hold keypoints (they are in result.keypoints). Rather than assembling the COCO JSON by hand, let validation write it for you:

from ultralytics import YOLO

# Load the model
model = YOLO("path/to/best.pt")

# Validate on the test split and save COCO-format predictions
metrics = model.val(data="coco-pose.yaml", split="test", save_json=True)

This writes predictions.json (image IDs, category IDs, boxes, scores and keypoints) to the validation run directory, for example runs/pose/val. Once you have the JSON file, you can zip it and submit it to the competition as specified. If you encounter further issues, please update the GitHub issue with the new error details.

Thank you for your response. I've run into an issue when executing the new code:

```python
from ultralytics import YOLO
import json
import os

# Load the model
model = YOLO("/root/lanyun-tmp/ultralytics-8.2.59/ultralytics/runs/train/C2F_CCA/weights/best.pt")

# Run inference on the test set
results = model.predict(source="/root/lanyun-tmp/ultralytics-8.1.45/ultralytics/assets/test0003")

# Convert results to COCO format
coco_results = []
for result in results:
    image_id = int(os.path.basename(result.path).split('.')[0])
    for box in result.boxes:
        coco_results.append({
            "image_id": image_id,
            "category_id": box.cls,
            "bbox": box.xywh.tolist(),
            "score": box.conf,
            "keypoints": box.keypoints.tolist()
        })

# Save results to JSON file
with open("coco_results.json", "w") as f:
    json.dump(coco_results, f)

The error I'm encountering is:

Traceback (most recent call last):
  File "coco-test.py", line 21, in <module>
    "keypoints": box.keypoints.tolist()
  File "/root/lanyun-tmp/ultralytics-8.2.59/ultralytics/utils/__init__.py", line 162, in __getattr__
    raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
AttributeError: 'Boxes' object has no attribute 'keypoints'. See valid attributes below.

Thank you and look forward to your reply

@xhf3571 it looks like the Boxes object does not have a keypoints attribute. This is likely because the model you are using is not a pose estimation model or the predictions do not include keypoints. Ensure you are using a pose-specific model like yolo26n-pose.pt. If the problem persists, please verify that your model and dataset are correctly configured for pose estimation. If you need further assistance, please update the issue with any new details.

Thank you for your reply. I used the official yolov8n-pose.pt but still encountered the same error. I referred to the issue link and raised a question in my own issue link. I am very confused about which method to use to correctly generate the JSON evaluation file. This seems like a small task, but I have been struggling with it for 5 days. Please help me, I would be extremely grateful.

Thank you very much, and I look forward to your response.

Thank you for your patience. Since you're using the official yolov8n-pose.pt model and still encountering issues, please ensure you have the latest version of the Ultralytics package. The error suggests that the Boxes object does not contain keypoints. Verify that your model is correctly configured for pose estimation and that your dataset paths are accurate. If the problem persists, please update your GitHub issue with any new details or errors. This will help us provide more targeted assistance.

I recently redownloaded the COCO-pose using the coco-pose.yaml configuration. I noticed that in the coco-pose/labels/val2017 directory, there are only 2346 .txt files, whereas the coco-pose/images/val2017 directory contains 5000 images. Is this discrepancy expected?

Additionally, I observed that coco-pose/labels/train2017 contains 56599 .txt files, but I encountered a failure while downloading the train2017 images. I plan to manually download these images and match them with the 56599 .txt files. Would this approach be appropriate?

Thank you for your assistance.

@xhf3571 hi there,

Thank you for your detailed observation and for bringing this to our attention.

The discrepancy you're noticing between the number of images and labels in the COCO-Pose dataset is indeed expected. The COCO dataset, including its pose estimation subset, often has more images than annotations because not all images contain the objects or keypoints of interest.

Val2017 Set

• Images: 5000
• Annotations: 2346

Train2017 Set

• Images: 118,287 (as per the full COCO dataset)
• Annotations: 56,599

For the val2017 set, the 5000 images include those without keypoint annotations, which explains why there are fewer .txt files. Similarly, for the train2017 set, the number of images and annotations can differ due to the same reason.

Downloading and Matching Images

Your approach to manually download the images and match them with the 56,599 .txt files is appropriate. Ensure that the images are correctly placed in the directory structure as specified in the coco-pose.yaml file.

path: ../datasets/coco-pose
train: train2017.txt
val: val2017.txt
test: test-dev2017.txt

Additional Resources

For more detailed information and to ensure you're using the latest dataset configuration, please refer to the COCO-Pose Dataset Documentation.

If you encounter any further issues or have additional questions, please provide a minimum reproducible example of your setup and code. This will help us assist you more effectively. You can find guidelines for creating a reproducible example here.

Thank you for your understanding and cooperation. Happy training! 🚀

Hi @aridas96,

Thank you for providing detailed information about your issue. It sounds like you've successfully trained your YOLOv8n-pose model, but you're encountering a problem with visualizing keypoint connections during inference.

To assist you better, could you please confirm the following:

1. Reproducible Code Example: Ensure that you provide a minimum reproducible code example. This helps us replicate the issue on our end and investigate a solution. You can refer to our guide on creating a minimum reproducible example here.

2. Package Versions: Verify that you are using the latest versions of torch and ultralytics. If not, please upgrade your packages and try again:

   pip install --upgrade torch ultralytics

Regarding the keypoint connections, it seems like you are already using the kpt_line=True parameter, which is correct. Here’s a quick example to ensure everything is set up properly:

from ultralytics import YOLO

# Load the trained model
model = YOLO("best.pt")

# Run inference on an image
results = model("path/to/your/image.jpg")

# Visualize the results with keypoint connections
for result in results:
    result.show(kpt_line=True)  # Ensure kpt_line is set to True to show connections

If you still face issues, please provide the specific code snippet you used for testing and any relevant details about your environment. This will help us diagnose the problem more effectively.

For more detailed information on pose estimation datasets and configurations, you can visit our Pose Estimation Datasets documentation.

Looking forward to your response!

@aridas96 hello,

Thank you for reaching out and for your detailed description of the issue you're encountering. It sounds like you've successfully trained your YOLOv8n-pose model, but you're not seeing the keypoint connections in your predictions.

To help you better, could you please confirm the following:

1. Reproducible Code Example: Ensure that you provide a minimum reproducible code example. This helps us replicate the issue on our end and investigate a solution. You can refer to our guide on creating a minimum reproducible example here.

2. Package Versions: Verify that you are using the latest versions of torch and ultralytics. If not, please upgrade your packages and try again:

   pip install --upgrade torch ultralytics

Regarding the keypoint connections, you can visualize them by ensuring that the show method is correctly configured to display the connections. Here's a quick example of how you can do this:

from ultralytics import YOLO

# Load the trained model
model = YOLO("path/to/your/trained/model.pt")

# Run inference on an image
results = model("path/to/your/image.jpg")

# Visualize the results with keypoint connections
for result in results:
    result.show(kpt_line=True)  # Ensure kpt_line is set to True to show connections

If you still face issues, please provide the specific code snippet you used for testing and any relevant details about your environment. This will help us diagnose the problem more effectively.

For more detailed information on pose estimation datasets and configurations, you can visit our Pose Estimation Datasets documentation.

Looking forward to your response!

@perry-smartsizer hi there!

Thank you for your question. Ultralytics YOLO pose does not support a different set of keypoints per class. A dataset has a single kpt_shape and a single flip_idx that apply to all classes, so a YAML with per-class kpt_shape will not work.

You have two options:

1. Train separate models (one dataset YAML per keypoint layout), for example one for person (17 keypoints) and one for tiger (12 keypoints).
2. Use one dataset with the maximum number of keypoints and pad unused keypoints with 0 0 0 (x, y, visibility), where visibility 0 marks them as missing.

Example YAML for the second option (person with 17 keypoints, tiger padded to 17):

path: ../datasets/multi-class-pose
train: images/train
val: images/val

kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] # must match your own keypoint ordering

names:
  0: person
  1: tiger

Every label row then has the form <class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> ... <px17> <py17> <p17-visibility>.

To train your model:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-pose.pt")  # load a pretrained model

# Train the model
results = model.train(data="multi-class-pose.yaml", epochs=100, imgsz=640)

Or via CLI:

yolo pose train data=multi-class-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640

For more detailed information on dataset preparation and configuration, please refer to our Pose Estimation Datasets Documentation.

I hope this helps! If you have any further questions, feel free to ask. 😊

Hello!

Yes, the COCO-Pose dataset used for keypoint detection with YOLOv8 is based directly on the COCO dataset available from the COCO website. Specifically, it leverages the COCO Keypoints 2017 images and labels to train models for pose estimation tasks.

For keypoint detection, it's indeed necessary for the images to have people present because the dataset supports 17 human keypoints to facilitate detailed pose estimation. There's no need to filter out images without people for this task, as the COCO-Pose dataset is already curated to include images annotated with keypoints relevant to pose estimation.

The Coco-Pose dataset does include images with people and has annotations for human keypoints. So, you don't need to filter out images without people. This dataset is specifically structured to aid in pose estimation tasks and includes a variety of images that are annotated for this purpose. Here’s a quick peek into how you can train a pose model with our dataset:

from ultralytics import YOLO

# Load a pretrained pose estimation model
model = YOLO("yolo26n-pose.pt")

# Train the model on the COCO-Pose dataset
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)

This command will use images that already have people in them and are labeled for pose estimation. If you need further details or assistance, feel free to ask. Happy modeling! 😊

Hello, could you please provide the download link of the coco-pose dataset, so that I can start to perform a replay of the key point detection? How did you come up with those evaluation indicators? What are the specific data set requirements and parameter configurations?

@WangDayeYa hey there! 🌟

For the COCO-Pose dataset, the labels are downloaded from Ultralytics and the images from the official COCO Dataset website. data=coco-pose.yaml does this automatically on first use, or you can run the same download yourself:

from pathlib import Path

from ultralytics.utils import ASSETS_URL
from ultralytics.utils.downloads import download

# Dataset root directory
dir = Path("../datasets/coco-pose")

# Labels
download([f"{ASSETS_URL}/coco2017labels-pose.zip"], dir=dir.parent)

# Images
urls = [
    "http://images.cocodataset.org/zips/train2017.zip",  # 118k training images
    "http://images.cocodataset.org/zips/val2017.zip",  # 5k validation images
]
download(urls, dir=dir / "images", threads=3)

The evaluation indicators, such as Object Keypoint Similarity (OKS), are standardized metrics provided by COCO to facilitate performance comparison across different models on pose estimation tasks.

For training on the COCO-Pose dataset, your dataset YAML file (coco-pose.yaml) is crucial. Here's how you'd train a YOLO26n-pose model for 100 epochs with images sized at 640:

yolo pose train data=coco-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640

This setup assumes coco-pose.yaml is properly pointing to your COCO-Pose dataset's directory and the dataset files.

Hope this gets you started on the right foot with keypoint detection! If you've got more questions, feel free to reach out. Happy detecting! 😊

Okay, According to your description, are you guys using the corresponding labeling information of train2017 and val2017 in the labels of coco2017labels-pose.zip in yolov5 to filter all the coco dataset images in order to get the coco-pose dataset for training.In other words, are you training and validating the dataset images corresponding to the labels file of coco2017labels-pose.zip in yolov5? may I ask how many rounds of training on the coco-pose dataset did you get your data results inside the document?Thank you.

@WangDayeYa hey there! 👋

Yes, you're correct! We use the labeling information from train2017 and val2017 found in the coco2017labels-pose.zip for filtering the COCO dataset images to obtain the COCO-Pose dataset. This enables our models, such as YOLO26n-pose, to be trained and validated specifically for pose estimation tasks using these annotated images.

Regarding the number of training epochs, the performance results documented are typically achieved after comprehensive training, often spanning hundreds of epochs to maximize accuracy and model effervescence. The specific number of epochs may vary depending on the model and training configurations.

For example, to train a pose estimation model like YOLO26n-pose, you might use:

yolo pose train data=coco-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640

This command initiates training with the COCO-Pose dataset, targeting 100 epochs.

Each model listed in the docs is trained to ensure optimal performance, with detailed evaluations conducted to generate the documentation results. For up-to-date performance metrics, it's a good idea to refer to our official documentation and experiment logs. 😊

Thanks for your question, and best of luck with your pose estimation projects!