Exporting trained yolov5 model (trained on custom dataset) to 'saved model' format changes the no. of classes and the name of classes to default coco128 values

ssingh17j commented 1 month ago

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YOLOv5 Component

Export

Bug

I trained yolov5s model to detect various logos (amazon, ups, fedex etc). The model detects the logos well. The command used for training is: python train.py --weights yolov5s.pt --epoch 100 --data C:\projects\logo_detector\yolov5\datasetv3\data.yaml

The command used for detecting logos is: python detect.py --weights best.pt --source 0

Screenshot of trained yolov5s model detecting the logos:

When I use export.py to convert the above model to saved model format, the model starts giving wrong output. The command used for exporting the model is: python export.py --weights best.pt --data C:\projects\logo_detector\yolov5\datasetv3\data.yaml --include saved_model

The command used for detection of logos using this saved model is: python detect.py --weights best_saved_model --source 0

Screenshot of yolov5s saved model giving wrong output is:

As far as I can understand, the model starts giving output according to the default coco128.yaml file. But I have not specified this file in my commands, so I cannot understand the reason behind this behaviour. Please let me know how to get correct output.

Environment

I have used the default git repository for yolov5
OS: Windows 10 Pro
Python: 3.12.3

Minimal Reproducible Example

No response

Additional

No response

Are you willing to submit a PR?

[ ] Yes I'd like to help by submitting a PR!

github-actions[bot] commented 1 month ago

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glenn-jocher commented 1 month ago

@ssingh17j hello,

Thank you for reaching out and providing detailed information about the issue you're encountering. It appears that the problem might be related to the class names and number of classes not being correctly transferred during the export process.

To address this, please ensure the following steps:

Verify Data Configuration: Make sure that the data.yaml file used during training is the same one used during export. This file should correctly define the number of classes and their names.

Check Export Command: Your export command looks correct, but let's ensure that the data.yaml is correctly referenced:

python export.py --weights best.pt --data C:\projects\logo_detector\yolov5\datasetv3\data.yaml --include saved_model

Update to Latest Version: Ensure you are using the latest version of YOLOv5 and its dependencies. Sometimes, issues are resolved in newer releases. You can update your repository and dependencies with:
```
git pull
pip install -r requirements.txt
```
Validate Exported Model: After exporting, you can validate the exported model to ensure it retains the correct class information. Use the val.py script to check the model's performance:
```
python val.py --weights best_saved_model --data C:\projects\logo_detector\yolov5\datasetv3\data.yaml
```
Inspect the Exported Model: You can use tools like Netron to visualize the exported model and verify that the class names and number of classes are correctly set.

If the issue persists, it might be helpful to debug the exported model. You can load the exported model in TensorFlow and inspect the class names and number of classes to ensure they match your custom dataset.

Here's a small snippet to load and inspect the TensorFlow SavedModel:

import tensorflow as tf

# Load the SavedModel
model = tf.saved_model.load('path/to/best_saved_model')

# Inspect the model's signature
print(model.signatures)

If you find any discrepancies or need further assistance, please feel free to share additional details, and we'll be happy to help you troubleshoot further.

ultralytics / yolov5