Update README.md

README.md

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----
-license: apache-2.0
----
+---
+tags:
+- image-classification
+- birder
+- pytorch
+library_name: birder
+license: apache-2.0
+---
+
+# Model Card for efficientvim_m1_il-common
+
+A EfficientViM image classification model. This model was trained on the `il-common` dataset, which contains common bird species found in Israel.
+
+The species list is derived from data available at <https://www.israbirding.com/checklist/>.
+
+## Model Details
+
+- **Model Type:** Image classification and detection backbone
+- **Model Stats:**
+    - Params (M): 6.1
+    - Input image size: 256 x 256
+- **Dataset:** il-common (371 classes)
+
+- **Papers:**
+    - EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality: <https://arxiv.org/abs/2411.15241>
+
+## Model Usage
+
+### Image Classification
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-common", 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)
+
+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, 371), representing class probabilities.
+```
+
+### Image Embeddings
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-common", 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)
+
+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, 320)
+```
+
+### Detection Feature Map
+
+```python
+from PIL import Image
+import birder
+
+(net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-common", 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)
+
+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, 128, 16, 16])),
+#  ('stage2', torch.Size([1, 192, 8, 8])),
+#  ('stage3', torch.Size([1, 320, 4, 4]))]
+```
+
+## Citation
+
+```bibtex
+@misc{lee2025efficientvimefficientvisionmamba,
+      title={EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality},
+      author={Sanghyeok Lee and Joonmyung Choi and Hyunwoo J. Kim},
+      year={2025},
+      eprint={2411.15241},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.15241},
+}
+```