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--- |
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license: mit |
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datasets: |
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- mlfoundations/datacomp_1b |
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pipeline_tag: feature-extraction |
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--- |
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# Model card for ViTamin-S-LTT |
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Official huggingface models of **ViTamin**, from the following CVPR 2024 paper: |
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[ViTamin: Design Scalable Vision Models in the Vision-language Era](https://arxiv.org/pdf/2404.02132.pdf).\ |
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✨  [Jieneng Chen](https://beckschen.github.io), [Qihang Yu](https://yucornetto.github.io/), [Xiaohui Shen](https://xiaohuishen.github.io/), [Alan Yuille](https://www.cs.jhu.edu/~ayuille/) and [Liang-Chieh Chen](http://liangchiehchen.com/)\ |
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🏠  Johns Hopkins University, Bytedance |
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Load from HuggingFace with transformers.AutoModel: |
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```python |
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import torch |
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import open_clip |
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from PIL import Image |
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from transformers import AutoModel, CLIPImageProcessor |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# obtained 69.1% zero-shot ImageNet score |
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model = AutoModel.from_pretrained( |
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'jienengchen/ViTamin-B', |
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trust_remote_code=True).to(device).eval() |
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image = Image.open('./image.png').convert('RGB') |
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image_processor = CLIPImageProcessor.from_pretrained('jienengchen/ViTamin-B') |
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pixel_values = image_processor(images=image, return_tensors='pt').pixel_values |
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pixel_values = pixel_values.to(torch.bfloat16).cuda() |
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tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K') |
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text = tokenizer(["a photo of vitamin", "a dog", "a cat"]).to(device) |
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with torch.no_grad(), torch.cuda.amp.autocast(): |
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image_features, text_features, logit_scale = model(pixel_values, text) |
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text_probs = (100.0 * image_features @ text_features.to(torch.float).T).softmax(dim=-1) |
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print("Label probs:", text_probs) |
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``` |
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## Main Results with CLIP Pre-training on DataComp-1B |
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| image encoder | image size | num patches | text encoder depth/width | seen samples (B) | trainable params Image+Text (M) | MACs Image+Text (G) | ImageNet Acc. | avg. 38 datasets | ImageNet dist. shift. | VTAB | retrieval | |
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|---------------|------------|-------------|--------------------------|-------------------|---------------------------------|----------------------|---------------|------------------|-----------------------|------|-----------| |
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| ViTamin-S | 224 | 196 | 12/384 | 1.28 | 22.0+40.4 | 5.50+1.64 | 62.2 | 53.2 | 51.3 | 51.7 | 50.0 | |
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| ViTamin-S-LTT | 224 | 196 | 12/384 | 1.28 | 22.0+40.4 | 5.50+1.64 | 63.4 | 54.6 | 51.6 | 54.9 | 52.9 | |
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| ViTamin-B | 224 | 196 | 12/512 | 1.28 | 87.5+63.4 | 21.8+2.9 | 68.9 | 57.7 | 58.3 | 56.4 | 54.1 | |
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| ViTamin-B-LTT | 224 | 196 | 12/512 | 1.28 | 87.5+63.4 | 21.8+2.9 | 70.8 | 59.4 | 59.3 | 56.6 | 59.4 | |
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| ViTamin-L | 224 | 196 | 12/768 | 12.8 | 333.3+123.7 | 72.6+6.6 | 80.8 | 66.7 | 69.8 | 65.3 | 60.3 | |
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| ViTamin-L | 256 | 256 | 12/768 | 12.8+0.2 | 333.4+123.7 | 94.8+6.6 | 81.2 | 67.0 | 71.1 | 65.3 | 61.2 | |
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| ViTamin-L | 336 | 441 | 12/768 | 12.8+0.2 | 333.6+123.7 | 163.4+6.6 | 81.6 | 67.0 | 72.1 | 64.4 | 61.6 | |
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| ViTamin-L | 384 | 576 | 12/768 | 12.8+0.2 | 333.7+123.7 | 213.4+6.6 | 81.8 | 67.2 | 72.4 | 64.7 | 61.8 | |
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| ViTamin-L2 | 224 | 196 | 24/1024 | 12.8 | 333.6+354.0 | 72.6+23.3 | 80.9 | 66.4 | 70.6 | 63.4 | 61.5 | |
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| ViTamin-L2 | 256 | 256 | 24/1024 | 12.8+0.5 | 333.6+354.0 | 94.8+23.3 | 81.5 | 67.4 | 71.9 | 64.1 | 63.1 | |
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| ViTamin-L2 | 336 | 441 | 24/1024 | 12.8+0.5 | 333.8+354.0 | 163.4+23.3 | 81.8 | 67.8 | 73.0 | 64.5 | 63.6 | |
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| ViTamin-L2 | 384 | 576 | 24/1024 | 12.8+0.5 | 334.0+354.0 | 213.4+23.3 | 82.1 | 68.1 | 73.4 | 64.8 | 63.7 | |
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| ViTamin-XL | 256 | 256 | 27/1152 | 12.8+0.5 | 436.1+488.7 | 125.3+33.1 | 82.1 | 67.6 | 72.3 | 65.4 | 62.7 | |
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| ViTamin-XL | 384 | 576 | 27/1152 | 12.8+0.5 | 436.1+488.7 | 281.9+33.1 | 82.6 | 68.1 | 73.6 | 65.6 | 63.8 | |
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| ViTamin-XL | 256 | 256 | 27/1152 | 40 | 436.1+488.7 | 125.3+33.1 | 82.3 | 67.5 | 72.8 | 64.0 | 62.1 | |
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| ViTamin-XL | 336 | 441 | 27/1152 | 40+1 | 436.1+488.7 | 215.9+33.1 | 82.7 | 68.0 | 73.9 | 64.1 | 62.6 | |
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| ViTamin-XL | 384 | 576 | 27/1152 | 40+1 | 436.1+488.7 | 281.9+33.1 | 82.9 | 68.1 | 74.1 | 64.0 | 62.5 | |
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## Main Results on Downstream tasks |
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**Open-Vocab Detection** |
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| image encoder | detector | OV-COCO (AP<sub>50</sub><sup>novel</sup>) | OV-LVIS (AP<sub>r</sub>) | |
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|---------------|----------|---------------------------------------|-----------------------| |
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| ViT-L/14 | Sliding F-ViT | 36.1 | 32.5 | |
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| ViTamin-L | Sliding F-ViT | 37.5 | 35.6 | |
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**Open-Vocab Segmentation** |
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| image encoder | segmentor | ADE | Cityscapes | MV | A-150 | A-847 | PC-459 | PC-59 | PAS-21 | |
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|---------------|-------------|----------------|--------------|------|-------|-------|--------|-------|--------------------| |
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| ViT-L/14 | Sliding FC-CLIP | 24.6 | 40.7 | 16.5 | 31.8 | 14.3 | 18.3 | 55.1 | 81.5 | |
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| ViTamin-L | Sliding FC-CLIP | 27.3 | 44.0 | 18.2 | 35.6 | 16.1 | 20.4 | 58.4 | 83.4 | |
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Note: Panoptic dataset (ADE, CityScapes, MV) are with the metric of PQ. Semantic dataset (A-150, A-847, PC-459, PC-59, PAS-21) are with the metric of mIoU. |
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**Large Multi-modal Models** |
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| image encoder | image size | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MME | MM-Bench | MM-B-CN | SEED | LLaVA-Wild | MM-Vet | |
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|---------------|----------|-------|------|--------|------|-------|------|------|----------|---------|------|------------|--------| |
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| ViTamin-L | 336 | 78.4 | 61.6 | 51.1 | 66.9 | 58.7 | 84.6 | 1421 | 65.4 | 58.4 | 57.7 | 64.5 | 33.6 | |
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| ViTamin-L | 384 | 78.9 | 61.6 | 55.4 | 67.6 | 59.8 | 85.5 | 1447 | 64.5 | 58.3 | 57.9 | 66.1 | 33.6 | |
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## Citing ViTamin |
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``` |
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@inproceedings{chen2024vitamin, |
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title={ViTamin: Design Scalable Vision Models in the Vision-language Era}, |
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author={Chen, Jieneng and Yu, Qihang and Shen, Xiaohui and Yuille, ALan and Chen, Liang-Chieh}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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year={2024} |
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} |
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``` |
