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'yolov8n-pose.yaml' #721

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palmcorp opened this issue Jun 11, 2024 · 19 comments
Open
1 task done

'yolov8n-pose.yaml' #721

palmcorp opened this issue Jun 11, 2024 · 19 comments
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question A HUB question that does not involve a bug

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where can i download 'yolov8n-pose.yaml

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@palmcorp palmcorp added the question A HUB question that does not involve a bug label Jun 11, 2024
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👋 Hello @palmcorp, thank you for raising an issue about Ultralytics HUB 🚀! Please visit our HUB Docs to learn more:

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@pderrenger
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Hello!

Thank you for reaching out. It looks like you're looking for the yolov8n-pose.yaml file. This file is part of the configuration files used for YOLOv8 models, specifically for pose estimation.

You can find the configuration files for YOLOv8, including yolov8n-pose.yaml, in the Ultralytics GitHub repository. Here is a direct link to the YOLOv8 configuration files.

If you have any further questions or need additional assistance, feel free to ask. We're here to help! 😊

@palmcorp
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palmcorp commented Jun 12, 2024 via email

@pderrenger
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Hi Paula,

Thank you for providing detailed information about your issue. Let's work through this together to find a solution.

Minimum Reproducible Example

To better assist you, could you please provide a minimum reproducible code example? This will help us understand the context and reproduce the issue on our end. You can refer to our documentation for guidance on creating one. It's crucial for us to be able to reproduce the bug before we can investigate a solution.

Version Check

Please ensure that you are using the most recent versions of torch, ultralytics, and hub-sdk. If not, please upgrade your packages and try again:

pip install --upgrade torch ultralytics hub-sdk

Label Format

Regarding the label format, YOLOv8 expects a specific structure for pose estimation. The kpt_shape parameter in your yolov8n-pose.yaml should match the number of keypoints in your dataset. If your dataset has 17 keypoints, you should set kpt_shape: [17, 2].

Pretrained Model

Loading a pretrained model should not interfere with your custom training as long as the label formats and configurations are correctly set. However, if you suspect it might be causing issues, you can try training without loading the pretrained model to see if it resolves the problem.

Example Configuration

Here’s an example of what your yolov8n-pose.yaml might look like:

# YOLOv8n-pose model configuration
kpt_shape: [17, 2]
...

Data.yaml

Ensure your data.yaml file is correctly formatted and includes the necessary parameters:

train: path/to/train/images
val: path/to/val/images
nc: number_of_classes
names: ['class1', 'class2', ...]

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

@palmcorp
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palmcorp commented Jun 12, 2024 via email

@palmcorp
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palmcorp commented Jun 13, 2024 via email

@palmcorp
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palmcorp commented Jun 13, 2024 via email

@pderrenger
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@palmcorp hi Paul,

Thank you for sharing the Colab notebook and the data5.yaml file. Let's dive into the issue and work towards a solution.

Key Points to Address

  1. Keypoint Shape Mismatch:
    Your data5.yaml specifies kpt_shape: [5,2], which indicates 5 keypoints. However, if your dataset actually contains 17 keypoints, you should update this to kpt_shape: [17,2].

  2. Configuration File:
    Ensure that your yolov8n-pose.yaml configuration file aligns with the number of keypoints in your dataset. Here’s an example configuration snippet:

    # YOLOv8n-pose model configuration
kpt_shape: [17, 2]
...

  3. Pretrained Model:
    If you are loading a pretrained model, ensure that it is compatible with your custom dataset configuration. If you suspect it might be causing issues, try training without loading the pretrained model to see if it resolves the problem.

Next Steps

  1. Update data5.yaml:
    Change the kpt_shape parameter to match the number of keypoints in your dataset:

    kpt_shape: [17, 2]  # number of keypoints, number of dimensions

  2. Verify Versions:
    Ensure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

    pip install --upgrade torch ultralytics hub-sdk

  3. Run the Notebook:
    After making the above changes, run your Colab notebook again to see if the issue persists.

Example Code

Here’s a simplified example of how your yolov8n-pose.yaml might look:

# YOLOv8n-pose model configuration
kpt_shape: [17, 2]
...

Additional Resources

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem. This will help us provide more targeted assistance.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

@palmcorp
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palmcorp commented Jun 14, 2024 via email

@pderrenger
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Hi Paul,

Thank you for the detailed update and for sharing your code and console output. Let's work through this step-by-step to identify and resolve the issue.

Key Observations

  1. Keypoint Shape: You mentioned updating data.yaml to data17.yaml. Ensure that kpt_shape in data17.yaml correctly reflects the number of keypoints in your dataset.
  2. Error Analysis: The error traceback indicates an IndexError: list index out of range, which suggests there might be a mismatch in the expected input dimensions or configurations.

Steps to Resolve

  1. Verify data17.yaml Configuration:
    Ensure that data17.yaml has the correct kpt_shape parameter:

    kpt_shape: [17, 2]  # number of keypoints, number of dimensions

  2. Check Model Configuration:
    Make sure your yolov8n-pose.yaml file aligns with the data17.yaml configuration. Here’s an example snippet:

    # YOLOv8n-pose model configuration
kpt_shape: [17, 2]
...

  3. Update and Verify Package Versions:
    Ensure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

    pip install --upgrade torch ultralytics hub-sdk

  4. Simplify Model Loading:
    Simplify your model loading function to isolate the issue. Here’s a revised version:

    from ultralytics import YOLO

def POSE_LOAD(data_yaml_path):
    model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
    return model

print("short start with Paula")
Data_Yaml_Path = "/content/yaml_files/data17.yaml"

if not os.path.isfile(Data_Yaml_Path):
    raise FileNotFoundError(f"{Data_Yaml_Path} not found")

model = POSE_LOAD(Data_Yaml_Path)

  5. Run the Notebook:
    After making the above changes, run your Colab notebook again to see if the issue persists.

Additional Debugging

If the issue persists, please provide the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

@palmcorp
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palmcorp commented Jun 14, 2024 via email

@pderrenger
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Hi Paul,

Thank you for your proactive approach! Yes, it is crucial that the kpt_shape: [17, 2] parameter is consistent across all relevant .yaml files to avoid any configuration mismatches. Ensuring uniformity in these settings will help prevent errors related to keypoint dimensions.

Steps to Follow

  1. Consistency Check:
    Verify that kpt_shape: [17, 2] is consistently set in all your configuration files, including data17.yaml and yolov8n-pose.yaml.

  2. Package Upgrades:
    Upgrading torch, ultralytics, and hub-sdk in every notebook is a great step. You can do this with:

    pip install --upgrade torch ultralytics hub-sdk

  3. Simplified Model Loading:
    Ensure your model loading function is straightforward to isolate any potential issues. Here’s a reminder of the simplified function:

    from ultralytics import YOLO

def POSE_LOAD(data_yaml_path):
    model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
    return model

print("short start with Paula")
Data_Yaml_Path = "/content/yaml_files/data17.yaml"

if not os.path.isfile(Data_Yaml_Path):
    raise FileNotFoundError(f"{Data_Yaml_Path} not found")

model = POSE_LOAD(Data_Yaml_Path)

Additional Debugging

If the issue persists after these steps, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

@palmcorp
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palmcorp commented Jun 15, 2024 via email

@pderrenger
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Hi Paul,

Thank you for your detailed message and for sharing the code snippets and error logs. Let's address the issues step-by-step to help you resolve them.

Key Observations and Steps to Resolve

  1. Corrupt Images/Labels:
    The warning indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. This discrepancy is likely causing the training to fail.

    • Verify Label Format: Ensure that your label files are correctly formatted. For pose estimation, each label should include the class and keypoint coordinates. If you are using 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns.
    • Check Dataset: Double-check your dataset to ensure that all images and labels are correctly paired and formatted.

  2. Configuration Consistency:
    Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

  3. Package Upgrades:
    Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

    pip install --upgrade torch ultralytics hub-sdk

  4. Simplified Model Loading:
    Simplify your model loading function to isolate the issue. Here’s a revised version:

    from ultralytics import YOLO

def POSE_LOAD():
    model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
    return model

print("short start with Paula")
model = POSE_LOAD()

Example Code for Label Verification

Here’s a small script to verify the format of your label files:

import os

def verify_labels(label_dir, expected_columns=39):
    for label_file in os.listdir(label_dir):
        if label_file.endswith('.txt'):
            with open(os.path.join(label_dir, label_file), 'r') as f:
                for line in f:
                    columns = line.strip().split()
                    if len(columns) != expected_columns:
                        print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.")
                        return False
    return True

label_dir = '/content/datasets/echo-pose/labels/train'
if verify_labels(label_dir):
    print("All label files are correctly formatted.")
else:
    print("Some label files are incorrectly formatted.")

Next Steps

  1. Verify Label Format: Run the above script to ensure your label files are correctly formatted.
  2. Consistency Check: Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.
  3. Upgrade Packages: Upgrade torch, ultralytics, and hub-sdk to the latest versions.
  4. Simplify Model Loading: Use the simplified model loading function provided above.

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

@palmcorp
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palmcorp commented Jun 16, 2024 via email

@pderrenger
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Hi Paul,

Thank you for your persistence and for providing additional context. Let's work through this together to identify and resolve the issue.

Key Observations

  1. Label Format:
    The error message indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. For pose estimation with 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns. It seems there might be a discrepancy here.

  2. Configuration Consistency:
    Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

  3. Package Versions:
    Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

    pip install --upgrade torch ultralytics hub-sdk

Steps to Resolve

  1. Verify Label Format:
    Run the following script to ensure your label files are correctly formatted:

    import os

def verify_labels(label_dir, expected_columns=35):
    for label_file in os.listdir(label_dir):
        if label_file.endswith('.txt'):
            with open(os.path.join(label_dir, label_file), 'r') as f:
                for line in f:
                    columns = line.strip().split()
                    if len(columns) != expected_columns:
                        print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.")
                        return False
    return True

label_dir = '/content/datasets/echo-pose/labels/train'
if verify_labels(label_dir):
    print("All label files are correctly formatted.")
else:
    print("Some label files are incorrectly formatted.")

  2. Consistency Check:
    Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.

  3. Simplify Model Loading:
    Use the simplified model loading function provided earlier to isolate the issue:

    from ultralytics import YOLO

def POSE_LOAD():
    model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
    return model

print("short start with Paula")
model = POSE_LOAD()

Additional Debugging

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

@palmcorp
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palmcorp commented Jun 16, 2024 via email

@palmcorp
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palmcorp commented Jun 17, 2024 via email

@pderrenger
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Hi Paul,

Thank you for your continued efforts and for sharing the updated label verification script. It's great to see your proactive approach in refining the solution. Let's address the current situation and ensure we get everything working smoothly.

Key Observations

  1. Label Format:
    Your label format seems correct with the class, bounding box, and keypoints. However, the error message indicates a requirement of 39 columns, which suggests there might be an additional dimension or configuration mismatch.

  2. Configuration Consistency:
    Ensuring kpt_shape: [17, 2] is consistent across all relevant .yaml files is crucial. It looks like you've already made this adjustment.

  3. Package Versions:
    You've upgraded the packages, which is excellent. This ensures compatibility and access to the latest features and fixes.

Steps to Resolve

  1. Enhanced Label Verification Script:
    Your updated script is a good start. Here’s a refined version that considers both 2D and 3D keypoints:

    import os

VERSION = "0.2"
KEYSIZE = 3  # FOR 2D keys, otherwise 4

def verify_labels(label_dir, expected_columns=35):
    print("Checking labels for:", label_dir)
    for label_file in os.listdir(label_dir):
        if label_file.endswith('.txt'):
            with open(os.path.join(label_dir, label_file), 'r') as f:
                for line in f:
                    columns = line.strip().split()
                    print("class=", columns[0])
                    print("B BOX=", columns[1:5])
                    HEADER = 5
                    Nkeypoints = int(len(columns[5:]) / KEYSIZE)
                    if Nkeypoints != expected_columns:
                        print(f"Label file {label_file} has {len(columns) - HEADER} columns, expected {expected_columns} columns.")
                        return False
    return True

print("Label checker V", VERSION, "\n")
label_dir = '/content/datasets/echo-pose/labels/train'
if not os.path.exists(label_dir):
    print(label_dir, "doesn't exist")

if verify_labels(label_dir):
    print("All label files are correctly formatted.")
else:
    print("Some label files are incorrectly formatted.")

  2. Check for Additional Dimensions:
    Ensure that your labels and configuration files are not expecting an additional dimension (e.g., visibility or confidence score for each keypoint). This might explain the discrepancy in the expected number of columns.

  3. Simplified Model Loading:
    Use the simplified model loading function to isolate the issue:

    from ultralytics import YOLO

def POSE_LOAD():
    model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
    return model

print("short start with Paula")
model = POSE_LOAD()

Additional Debugging

If the issue persists, please provide the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

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