Unexpectedly large trained model size (~200 MB .pt and ~400 MB .onnx) about yolov3 HOT 4 CLOSED

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pip install ultralytics

from yolov3.

@Shassk hello! Thanks for reaching out with your observations. The size discrepancy you're seeing in the YOLOv3 ONNX model compared to v5 and v8 is indeed unusual. Here are a few things to consider:

1. Model Complexity: YOLOv3 has a different architecture compared to v5 and v8, which might inherently lead to different model sizes. However, the difference should not be as drastic as you've described.

2. Export Settings: When exporting to ONNX, ensure that you're using the same settings, such as --simplify, which can reduce the model size by eliminating redundant operations.

3. Training Configuration: Double-check your training configuration. Differences in layer configurations or model depth can lead to larger models.

4. Pruning: If model size is a critical factor, consider applying model pruning techniques before exporting to ONNX. This can help reduce the size and complexity of the model.

5. Optimization: Post-training optimization techniques can also be applied to the ONNX model to reduce its size.

If you've ensured all the above and the issue persists, it might be worth looking into the specifics of how the ONNX model is being saved. Sometimes, additional metadata or training information can bloat the file size.

For further assistance, please refer to our documentation or consider opening an issue with detailed information about your training configuration and export process. We're here to help! 😊

from yolov3.

It's not so much about export size as it is about primary trained model size in .pt format — 200 MB is not what I expected. But sure, I will create a new issue with all the data.

from yolov3.

@Shassk apologies for the confusion, and thank you for your patience. A 200 MB .pt file for YOLOv3 is indeed larger than typical. Here are a few quick checks you can do:

1. Model Architecture: Verify that the model architecture in the .yaml file matches the expected YOLOv3 architecture without unintended modifications.

2. Weights: Ensure that the model isn't accidentally saving additional weights or data that it shouldn't be.

3. Optimizer State: The .pt file includes both the model weights and the optimizer state. A large optimizer state could inflate the file size.

4. Precision: Check if the model is being saved with higher precision (e.g., float64) than necessary (float32 is standard).

If these checks don't reveal any issues, please do open a new issue with the details of your training setup, and we'll take a closer look to help resolve this. Your feedback is invaluable in improving the tools we provide to the community. 🌟

from yolov3.