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 hornet_tiny_7x7_danube-delta
+
+A HorNet image classification model. This model was trained on the `danube-delta` dataset (all the relevant bird species found int the Danube Delta region).
+
+The species list is derived from data available at <https://www.discoverdanubedelta.com/wp-content/uploads/2023/01/BirdsList-ian-2023.pdf>.
+
+Note: this is a subset of the `eu-common` dataset.
+
+## Model Details
+
+- **Model Type:** Image classification and detection backbone
+- **Model Stats:**
+    - Params (M): 22.1
+    - Input image size: 256 x 256
+- **Dataset:** danube-delta (368 classes)
+
+- **Papers:**
+    - HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions: <https://arxiv.org/abs/2207.14284>
+
+## Model Usage
+
+### Image Classification
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("hornet_tiny_7x7_danube-delta", 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, 368), representing class probabilities.
+```
+
+### Image Embeddings
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("hornet_tiny_7x7_danube-delta", 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, 512)
+```
+
+### Detection Feature Map
+
+```python
+from PIL import Image
+import birder
+
+(net, model_info) = birder.load_pretrained_model("hornet_tiny_7x7_danube-delta", 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, 64, 64, 64])),
+#  ('stage2', torch.Size([1, 128, 32, 32])),
+#  ('stage3', torch.Size([1, 256, 16, 16])),
+#  ('stage4', torch.Size([1, 512, 8, 8]))]
+```
+
+## Citation
+
+```bibtex
+@misc{rao2022hornetefficienthighorderspatial,
+      title={HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions},
+      author={Yongming Rao and Wenliang Zhao and Yansong Tang and Jie Zhou and Ser-Nam Lim and Jiwen Lu},
+      year={2022},
+      eprint={2207.14284},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2207.14284},
+}
+```