I want to do transfer learning with YOLOv8, could you please refer to the specific practices about ultralytics

[glenn-jocher]

@KaedeharaT certainly! For transfer learning with YOLOv8, leveraging the idea of starting from a pre-trained model and further training it on your targeted dataset (Company B's drawings) is a promising approach. Given the discrepancies in styles between Company A and B, it's essential to adapt the model for better performance.

Here's a brief step-by-step guide on how to proceed:

1. Start with a Pre-trained Model: Begin by loading a YOLOv8 model that has been pre-trained on a large dataset. This helps in having a good feature extraction base.

   from ultralytics import YOLO
   model = YOLO('yolov8n.pt')  # Load YOLOv8n or another suitable pre-trained model

2. Partial Freezing (Optional): If you're planning on freezing layers, you can freeze all but the last few layers, allowing the final layers to learn features specific to Company B's styles.

   model.freeze()  # Example method to freeze layers, adjust according to actual API

3. Fine-tune on Company B's Dataset: Train the model on your dataset comprising drawings from Company B. Adjust the epochs, batch size, and other hyperparameters based on your dataset size and the computational resources at your disposal.

   results = model.train(data='companyB_data.yaml', epochs=30)

4. Monitor and Adjust: Keep an eye on the validation metrics. If the transfer learning process is not yielding the expected improvements, consider unfreezing more layers or tweaking other training hyperparameters.

5. Domain Adaptation & Zero-Shot Learning: These are more advanced strategies that might require custom implementation or adjustments. Domain adaptation could involve training with mixed datasets from both companies or employing techniques to minimize domain shifts. For zero-shot learning, adding textual descriptors or leveraging a dataset with broader classes might help.

The key to successful transfer learning with YOLOv8 is experimentation and iterative refinement based on performance metrics. Since each dataset and task is unique, the optimal settings and strategies will vary. Good luck with improving your model's detection rate for Company B's architectural drawings! 🚀

[KaedeharaT]

After completing training on a dataset based on Company A's drawings and generating a best.pt file, I then loaded this file to train on Company B's dataset (which has a few annotations). During training, I tried freezing 5, 10, and 15 layers to compare the results, but none were ideal. If too few layers are frozen, the new best.pt file significantly reduces the recognition rate on Company A's drawings, which I think might be due to too many layers undergoing parameter changes during training. On the other hand, if too many layers are frozen, the new best.pt file produces many erroneous detections for both companies (e.g., a round table in the drawing is mistakenly detected as a baselinemark). I trained it like this:

yolo detect train data=D:\dynamoFile\PythonScript\generalization\dataset\data.yaml model=D:/dynamoFile/PythonScript/runs/detect/train14/weights/best.pt epochs=100 imgsz=1000 device=0 freeze=5,

where train14 is the model trained from Company A's drawings.
Could you tell me where my approach might be going wrong? Is changing the learning rate key? I am planning to try that now.

[glenn-jocher]

Hey there! 🌟 It looks like you're on the right path with experimenting on layer freezing to achieve the right balance for transfer learning between Company A and B's datasets. Freezing layers can indeed be tricky; finding the sweet spot requires a bit of tinkering.

Your intuition about changing the learning rate is definitely worth exploring! A too high learning rate might cause overfitting to Company B's dataset, reducing performance on Company A's drawings. Conversely, a very low learning rate can slow down the adaptation or lead to underfitting. Here’s a quick suggestion to experiment with:

• Try a gradual unfreezing approach: Start training by freezing a larger portion of the layers, and gradually unfreeze layers in stages, allowing the model more flexibility to adapt as training progresses.
• Adjust the learning rate dynamically: Start with a lower learning rate when more layers are frozen, and increase it as you unfreeze more layers. This can help in adapting the model gently to the new dataset.

Example snippet:

# Assuming 'freeze' and 'lr' can be dynamically adjusted (this is a conceptual example)
model.train(data='path/to/companyB_data.yaml', epochs=100, imgsz=1000, device=0, freeze=15, lr=0.001)

Remember, the key is in experimentation and slowly tweaking parameters based on your observations. Good luck! 🚀