Vision Transformer (relative position)

Category: ViT · Code: timm/models/vision_transformer_relpos.py · Architectures: 14 · Pretrained weights: 9 (9 with ImageNet-1k results)

Vision Transformers using relative position biases instead of absolute position embeddings. The module docstring marks these models as experimental.

Papers

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Swin Transformer V2: Scaling Up Capacity and Resolution

Usage

See the Quickstart for loading pretrained weights, image preprocessing and fine-tuning, and Feature Extraction for using the models as backbones.

>>> import timm
>>> model = timm.create_model('vit_relpos_small_patch16_224.sw_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)

Model API support

✓ supported, ✓ (square) works for square sizes only, ✗ not supported, – not needed (the model already accepts any input size). Checked by running each feature on a randomly initialized model (VisionTransformerRelPos with vit_srelpos_small_patch16_224 at 224), other architectures of the same class are expected to behave the same. See the Model API page for details.

Architectures

GMACs and parameter counts are for the default input size and classifier of each architecture, from the benchmark results.

Pretrained weights

Top-1 is ImageNet-1k validation accuracy from results-imagenet.csv at the listed eval size, - if the weight has no ImageNet-1k result (e.g. pretraining or image-text weights). Classes is the size of the pretrained classifier, none for weights without one.