Fine-Tuned Agglomerative Token Clustering - DeiT-Small-Average - ImageNet-1k
Model Details
Agglomerative Token Clustering (ATC), a novel hierarchical hard-merging based token reduction method.
- Developed by: Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, and Thomas B. Moeslund
- Model type: Vision Transformer
- License: MIT
- Task: Image Classification
Model Card
- Backbone: DeiT-Small
- Token Reduction Method: ATC
- Linkage Function: Average
- Reduction Ratio: {0.25, 0.5, 0.7, 0.9}
- Reduction Stages: 3, 6, 9
More Resources
- Repository: https://github.com/JoakimHaurum/ATC
- Paper: https://arxiv.org/abs/2409.11923
- Project Page: https://vap.aau.dk/atc
- HuggingFace Collection: https://huggingface.co/collections/joakimbh/agglomerative-token-clustering-66e94dfb313e85ec97590fe4
Use
The model files contain both standard and EMA model parameters. The version which gave the best performance is indicated with the "ema_best" boolean.