Update README.md

README.md

@@ -1,3 +1,102 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-text-matching
+model-index:
+  - name: SPEC-CLIP-ViT-B-32
+    results:
+      - task:
+          type: image-text-matching
+        dataset:
+          name: SPEC
+          type: compositional-reasoning
+        metrics:
+          - name: Absolute Size I2T
+            type: Image to Text Matching
+            value: 68.9
+          - name: Absolute Size T2I
+            type: Image to Text Matching
+            value: 60.7
+          - name: Relative Size I2T
+            type: Image to Text Matching
+            value: 40.3
+          - name: Relative Size T2I
+            type: Image to Text Matching
+            value: 44.1
+          - name: Absolute Position I2T
+            type: Image to Text Matching
+            value: 30.6
+          - name: Absolute Position T2I
+            type: Image to Text Matching
+            value: 34.2
+          - name: Relative Position I2T
+            type: Image to Text Matching
+            value: 46.6
+          - name: Relative Position T2I
+            type: Image to Text Matching
+            value: 46.9
+          - name: Existence I2T
+            type: Image to Text Matching
+            value: 83.4
+          - name: Existence T2I
+            type: Image to Text Matching
+            value: 53.1
+          - name: Count I2T
+            type: Image to Text Matching
+            value: 55.6
+          - name: Count T2I
+            type: Image to Text Matching
+            value: 57.8
+        source:
+          name: SPEC paper
+          url: https://arxiv.org/pdf/2312.00081
+---
+# SPEC-CLIP-ViT-B-32
+
+### Model Sources
+[**Code**](https://github.com/wjpoom/SPEC) | [**Paper**](https://huggingface.co/papers/2312.00081) | [**arXiv**](https://arxiv.org/abs/2312.00081)
+
+### Model Usage
+* download checkpoint
+```shell
+huggingface-cli download wjpoom/SPEC-CLIP-ViT-B-32 --local-dir checkpoints/SPEC-CLIP-ViT-B-32
+```
+
+* load model
+```python
+# pip install open_clip_torch
+import torch
+from PIL import Image
+import open_clip
+
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='checkpoints/SPEC-CLIP-ViT-B-32', load_weights_only=False)
+model.eval()  # model in train mode by default, impacts some models with BatchNorm or stochastic depth active
+tokenizer = open_clip.get_tokenizer('ViT-B-32')
+
+image = preprocess(Image.open("docs/CLIP.png")).unsqueeze(0)
+text = tokenizer(["a diagram", "a dog", "a cat"])
+
+with torch.no_grad(), torch.autocast("cuda"):
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+
+    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+
+print("Label probs:", text_probs)  # prints: [[1., 0., 0.]]
+```
+
+## Contact
+Feel free to contact us if you have any questions or suggestions 
+- Email (Wujian Peng): wjpeng24@m.fudan.edu.cn
+
+## Citation
+``` bibtex
+@inproceedings{peng2024synthesize,
+  title={Synthesize diagnose and optimize: Towards fine-grained vision-language understanding},
+  author={Peng, Wujian and Xie, Sicheng and You, Zuyao and Lan, Shiyi and Wu, Zuxuan},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={13279--13288},
+  year={2024}
+}
+```