active-learning-wildlife

Active learning workflows for wildlife detection use the HILDA framework to minimize annotation effort by training a model, predicting on unlabeled images, selecting uncertain samples, and exporting them to CVAT or Label Studio for expert correction before retraining.

Active learning minimizes human annotation effort by using a model to predict on unlabeled wildlife images, then intelligently selecting only the most uncertain or diverse samples for an expert to review and correct in annotation tools like CVAT or Label Studio.

Yes, you can use CVAT or Label Studio for pre-annotations within the wildlife detection active learning loop. The HILDA framework integrates with both tools to export model predictions for review and download corrected annotations for retraining.

Available sampling strategies for selecting wildlife images include a three-way family: RGB Contrast, Embedding Clustering, and Logit Uncertainty. These approaches optimize data efficiency by identifying the most informative samples for annotation.

To start a wildlife active learning experiment, you need unlabeled image data, an optional initial model, and defined annotation budgets. The workflow outputs generated training data, learning curves, and corrected annotations for iterative model improvement.