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 mvit_v2_t_il-all
+
+MViTv2 image classification model. This model was trained on the `il-all` dataset (all the relevant bird species found in Israel inc. rarities).
+
+The specie list is based on data from <https://www.israbirding.com/checklist/>.
+
+## Model Details
+
+- **Model Type:** Image classification / detection backbone
+- **Model Stats:**
+  - Params (M): 23.9
+  - Image size: 384 x 384
+- **Dataset:** il-all (550 classes)
+
+- **Papers:**
+  - MViTv2: Improved Multiscale Vision Transformers for Classification and Detection: <https://arxiv.org/abs/2112.01526>
+
+## 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("mvit_v2_t_il-all", 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("mvit_v2_t_il-all", 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("mvit_v2_t_il-all", 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{li2022mvitv2improvedmultiscalevision,
+      title={MViTv2: Improved Multiscale Vision Transformers for Classification and Detection}, 
+      author={Yanghao Li and Chao-Yuan Wu and Haoqi Fan and Karttikeya Mangalam and Bo Xiong and Jitendra Malik and Christoph Feichtenhofer},
+      year={2022},
+      eprint={2112.01526},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2112.01526}, 
+}
+```