_strategies - LizbethMG/DLC_PoseAnalysis GitHub Wiki

Detecting and preventing overfitting in a DeepLabCut (DLC) model are crucial steps to ensure the reliability and accuracy of your pose estimation results. Overfitting occurs when the network learns the training data too well, including its noise and outliers, leading to poor performance on unseen data. Here’s how you can identify overfitting and strategies to avoid it:

Identifying Overfitting in DeepLabCut

Poor Generalization: If your model performs exceptionally well on the training data but poorly on the validation or test data, it's a classic sign of overfitting.

Inconsistent Results Across Datasets: Overfitted models often fail to generalize across different datasets. If your model's performance significantly drops when evaluated on a new dataset, it might be overfitted.

High Variance in Performance: Overfitting can lead to high variance in the model's performance, where small changes in the input data lead to large changes in the output.

Strategies to Avoid Overfitting

Data Augmentation: Increase the diversity of your training data (there are some transformations that you could consider doing on your videos). This can help the model learn more generalized features rather than memorizing the training data. When creating training datasets there are a few options for augmentation methods to use. Check this flipping-keypoint augmentation example

Using an intermediate snapshot: To identify the optimal snapshot (the one with the best performance) specify 'all' in the snapshots section of the config.yaml file.

Increase Training Data: More data can help the model learn better generalizations. If possible, add more varied and representative data to your training set.

Fine-tuning and Transfer Learning: Start with a pre-trained model and fine-tune it on your specific task. Pre-trained models have already learned general features, which can reduce the risk of overfitting on your specific dataset.

If your model performs poorly on new videos, you can try extracting poorly tracked outlier frames and refine them manually to feed them back to the training dataset for a new round of training.

Here is a nice notebook explaining the steps :https://guillermohidalgogadea.com/openlabnotebook/refining-your-dlc-model/