Hi Mario,
When using a pretrained network like YOLO v2 on your own dataset, it's important to ensure that your model generalizes well to new, unseen data. If your network performs well only on the images in your training, validation, and test sets, but not on other images, there are several factors and strategies you can consider to improve generalization:
1. Increase Dataset Size:
- Data Augmentation: Use techniques such as rotation, scaling, flipping, cropping, and color jittering to artificially increase the size and diversity of your dataset. This can help the model learn more robust features.
- Collect More Data: If possible, gather more labeled images that cover a wider variety of scenarios and conditions.
2. Improve Labels and Annotations:
- Ensure that your labels are accurate and consistent. Poor labeling can lead to poor model performance.
3. Fine-Tuning the Model:
- Adjust Learning Rate: Experiment with different learning rates. A learning rate that is too high might cause the model to converge too quickly to a suboptimal solution.
- More Epochs: Train for more epochs to allow the model to learn better representations, but watch out for overfitting.
4. Regularization Techniques:
- Use techniques like dropout, weight decay, or early stopping to prevent overfitting.
5. Evaluate and Adjust the Model:
- Validation: Regularly validate the model on a separate validation set to monitor its performance and adjust hyperparameters accordingly.
- Error Analysis: Analyze where the model is failing on new images. This can give insights into what features or scenarios the model is not capturing well.
6. Test on New Data:
- Ideally, you should test your model on a completely separate dataset that was not used during training or validation. This can give you a better indication of how well your model generalizes to new data.
By applying these strategies, you should be able to improve the generalization of your YOLO v2 model and achieve better performance on new, unseen images.
Hope this helps.
