Update README.md

README.md

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 ---
+tags:
+- image-feature-extraction
+- birder
+- pytorch
+library_name: birder
 license: apache-2.0
+base_model:
+- QuanSun/EVA-CLIP
 ---
+
+# Model Card for rope_i_vit_l14_nf_swiglu_c1_eva02-clip
+
+A RoPE ViT-L14 image encoder from the EVA02 CLIP model by Sun et al., converted to the Birder format for image feature extraction.
+This version retains the original model weights and architecture.
+It is a general-purpose visual backbone.
+
+See: <https://huggingface.co/QuanSun/EVA-CLIP> for further details.
+
+## Model Details
+
+- **Model Type:** Image classification and detection backbone
+- **Model Stats:**
+    - Params (M): 304.5
+    - Input image size: 336 x 336
+
+- **Papers:**
+    - EVA-02: A Visual Representation for Neon Genesis: <https://arxiv.org/abs/2303.11331>
+    - EVA-CLIP: Improved Training Techniques for CLIP at Scale: <https://arxiv.org/abs/2303.15389>
+
+## Model Usage
+
+### Image Classification
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+# Option 1: manual setup (more control over preprocessing)
+net, model_info = birder.load_pretrained_model("rope_i_vit_l14_nf_swiglu_c1_eva02-clip", inference=True)
+
+# Get the image size the model was trained on
+size = birder.get_size_from_signature(model_info.signature)
+
+# Create an inference transform
+transform = birder.classification_transform(size, model_info.rgb_stats)
+
+# Option 2: helper (quick start with default preprocessing)
+net, model_info, transform = birder.load_pretrained_model_and_transform("rope_i_vit_l14_nf_swiglu_c1_eva02-clip", inference=True)
+
+image = "path/to/image.jpeg"  # or a PIL image, must be loaded in RGB format
+out, _ = infer_image(net, image, transform)
+# out is a NumPy array with shape of (1, 768), representing class probabilities.
+```
+
+### Image Embeddings
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+# Option 1: manual setup (more control over preprocessing)
+net, model_info = birder.load_pretrained_model("rope_i_vit_l14_nf_swiglu_c1_eva02-clip", inference=True)
+
+# Get the image size the model was trained on
+size = birder.get_size_from_signature(model_info.signature)
+
+# Create an inference transform
+transform = birder.classification_transform(size, model_info.rgb_stats)
+
+# Option 2: helper (quick start with default preprocessing)
+net, model_info, transform = birder.load_pretrained_model_and_transform("rope_i_vit_l14_nf_swiglu_c1_eva02-clip", inference=True)
+
+image = "path/to/image.jpeg"  # or a PIL image
+out, embedding = infer_image(net, image, transform, return_embedding=True)
+# embedding is a NumPy array with shape of (1, 1024)
+```
+
+### Detection Feature Map
+
+```python
+from PIL import Image
+import birder
+
+net, model_info, transform = birder.load_pretrained_model_and_transform("rope_i_vit_l14_nf_swiglu_c1_eva02-clip", inference=True)
+
+image = Image.open("path/to/image.jpeg")
+features = net.detection_features(transform(image).unsqueeze(0))
+# features is a dict (stage name -> torch.Tensor)
+print([(k, v.size()) for k, v in features.items()])
+# Output example:
+# [('stage1', torch.Size([1, 1024, 24, 24]))]
+```
+
+## Citation
+
+```bibtex
+@article{Fang_2024,
+   title={EVA-02: A visual representation for neon genesis},
+   volume={149},
+   ISSN={0262-8856},
+   url={http://dx.doi.org/10.1016/j.imavis.2024.105171},
+   DOI={10.1016/j.imavis.2024.105171},
+   journal={Image and Vision Computing},
+   publisher={Elsevier BV},
+   author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
+   year={2024},
+   month=Sept, pages={105171}
+}
+@misc{sun2023evaclipimprovedtrainingtechniques,
+      title={EVA-CLIP: Improved Training Techniques for CLIP at Scale},
+      author={Quan Sun and Yuxin Fang and Ledell Wu and Xinlong Wang and Yue Cao},
+      year={2023},
+      eprint={2303.15389},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2303.15389},
+}
+```