update

README.md

@@ -1,3 +1,97 @@
 ---
 license: mit
+datasets:
+- mlfoundations/datacomp_1b
+pipeline_tag: feature-extraction
 ---
+
+# Model card for ViTamin-XL-336px
+
+Official huggingface models of **ViTamin**, from the following CVPR 2024 paper:
+
+[ViTamin: Design Scalable Vision Models in the Vision-language Era](https://arxiv.org/pdf/2404.02132.pdf).\
+✨  [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/)\
+🏠  Johns Hopkins University, Bytedance
+
+
+Load from HuggingFace with transformers.AutoModel:
+```python
+import torch
+import open_clip
+from PIL import Image
+from transformers import AutoModel, CLIPImageProcessor
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModel.from_pretrained(
+    'jienengchen/ViTamin-XL-336px',
+    trust_remote_code=True).to(device).eval()
+
+image = Image.open('./image.png').convert('RGB')
+image_processor = CLIPImageProcessor.from_pretrained('jienengchen/ViTamin-XL-336px')
+
+pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
+pixel_values = pixel_values.to(torch.bfloat16).cuda()
+
+tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K')
+text = tokenizer(["a photo of vitamin", "a dog", "a cat"]).to(device)
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features, text_features, logit_scale = model(pixel_values, text)
+    text_probs = (100.0 * image_features @ text_features.to(torch.float).T).softmax(dim=-1)
+
+print("Label probs:", text_probs)
+```
+
+## Main Results with CLIP Pre-training on DataComp-1B
+
+
+| 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 |
+|---------------|------------|-------------|--------------------------|-------------------|---------------------------------|----------------------|---------------|------------------|-----------------------|------|-----------|
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+| 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      |
+
+## Main Results on Downstream tasks
+**Open-Vocab Detection**
+| image encoder | detector   | OV-COCO (AP<sub>50</sub><sup>novel</sup>) | OV-LVIS (AP<sub>r</sub>) |
+|---------------|----------|---------------------------------------|-----------------------|
+| ViT-L/14      | Sliding F-ViT | 36.1                                  | 32.5                  |
+| ViTamin-L     | Sliding F-ViT  | 37.5                                | 35.6              |
+
+**Open-Vocab Segmentation**
+
+| image encoder | segmentor      | ADE            | Cityscapes   | MV   | A-150 | A-847 | PC-459 | PC-59 | PAS-21             |
+|---------------|-------------|----------------|--------------|------|-------|-------|--------|-------|--------------------|
+| ViT-L/14      | Sliding FC-CLIP  | 24.6           | 40.7         | 16.5 | 31.8  | 14.3  | 18.3   | 55.1  | 81.5               |
+| ViTamin-L     | Sliding FC-CLIP  | 27.3           | 44.0     | 18.2 | 35.6 | 16.1 | 20.4 | 58.4 | 83.4 |
+
+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.
+
+**Large Multi-modal Models**
+
+| image encoder      | image size | VQAv2 | GQA  | VizWiz | SQA  | T-VQA | POPE | MME  | MM-Bench | MM-B-CN | SEED | LLaVA-Wild | MM-Vet |
+|---------------|----------|-------|------|--------|------|-------|------|------|----------|---------|------|------------|--------|
+| ViTamin-L   | 224 | 78.4  | 61.6 | 51.1   | 66.9 | 58.7  | 84.6 | 1421 | 65.4     | 58.4    | 57.7 | 64.5       | 33.6   |
+| 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   |
+
+
+## Citing ViTamin
+
+```
+@inproceedings{chen2024vitamin,
+  title={ViTamin: Design 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}
+}
+```