Summary of Transfer Learning with Self-supervised Vision Transformers For Snake Identification, by Anthony Miyaguchi et al.
Transfer Learning with Self-Supervised Vision Transformers for Snake Identification
by Anthony Miyaguchi, Murilo Gustineli, Austin Fischer, Ryan Lundqvist
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an approach to predict snake species from images, specifically for the SnakeCLEF 2024 competition. The authors use Meta’s DINOv2 vision transformer model for feature extraction, leveraging its ability to tackle high variability and visual similarity in a dataset of 182,261 images. They perform exploratory analysis on embeddings to understand their structure and train a linear classifier on the embeddings to predict species. Despite achieving a score of 39.69, the results show promise for DINOv2 embeddings in snake identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper predicts snake species from images using Meta’s DINOv2 model. It helps identify snakes by analyzing image features. The authors test how well this works and share their code so others can try it too! |
Keywords
* Artificial intelligence * Feature extraction * Vision transformer
