Update README.md

README.md

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----
-license: apache-2.0
----
+---
+tags:
+- image-classification
+- birder
+library_name: birder
+license: apache-2.0
+---
+
+# Model Card for regnet_y_8g_intermediate-eu-common
+
+RegNet Y image classification model. The model follows a two-stage training process: first undergoing intermediate training on a large-scale dataset containing diverse bird species from around the world, then fine-tuned specifically on the `eu-common` dataset containing common European bird species.
+
+The species list is derived from the Collins bird guide [^1].
+
+[^1]: Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins.
+
+## Model Details
+
+- **Model Type:** Image classification and detection backbone
+- **Model Stats:**
+  - Params (M): 38.8
+  - Input image size: 384 x 384
+- **Dataset:** eu-common (708 classes)
+
+- **Papers:**
+  - Designing Network Design Spaces: <https://arxiv.org/abs/2003.13678>
+
+## Model Usage
+
+### Image Classification
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("regnet_y_8g_intermediate-eu-common", inference=True)
+
+# Get the image size the model was trained on
+size = birder.get_size_from_signature(signature)
+
+# Create an inference transform
+transform = birder.classification_transform(size, rgb_stats)
+
+image = "path/to/image.jpeg"  # or a PIL image
+(out, _) = infer_image(net, image, transform)
+# out is a NumPy array with shape of (1, num_classes)
+```
+
+### Image Embeddings
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("regnet_y_8g_intermediate-eu-common", inference=True)
+
+# Get the image size the model was trained on
+size = birder.get_size_from_signature(signature)
+
+# Create an inference transform
+transform = birder.classification_transform(size, 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, embedding_size)
+```
+
+### Detection Feature Map
+
+```python
+from PIL import Image
+import birder
+
+(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("regnet_y_8g_intermediate-eu-common", inference=True)
+
+# Get the image size the model was trained on
+size = birder.get_size_from_signature(signature)
+
+# Create an inference transform
+transform = birder.classification_transform(size, 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, 96, 96, 96])),
+#  ('stage2', torch.Size([1, 192, 48, 48])),
+#  ('stage3', torch.Size([1, 384, 24, 24])),
+#  ('stage4', torch.Size([1, 768, 12, 12]))]
+```
+
+## Citation
+
+```bibtex
+@misc{radosavovic2020designingnetworkdesignspaces,
+      title={Designing Network Design Spaces}, 
+      author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
+      year={2020},
+      eprint={2003.13678},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2003.13678}, 
+}
+```