ViTamin

Category: Hybrid · Code: timm/models/vitamin.py · Architectures: 13 · Pretrained weights: 15 (0 with ImageNet-1k results)

ViTamin is a vision model designed for vision-language models, with convolutional (MBConv) stages followed by transformer stages. The weights are CLIP image towers.

Papers

• ViTamin: Designing Scalable Vision Models in the Vision-Language Era

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('vitamin_base_224.datacomp1b_clip', 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 (VisionTransformer with vitamin_small_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.

Citation

@inproceedings{chen2024vitamin,
  title={ViTamin: Designing Scalable Vision Models in the Vision-language Era},
  author={Chen, Jieneng and Yu, Qihang and Shen, Xiaohui and Yuille, Alan and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}