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 vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-common
+
+A ViT Parallel s16 18x2 image classification model. The model follows a three-stage training process: first, data2vec pretraining, next intermediate training on a large-scale dataset containing diverse bird species from around the world, finally 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): 64.7
+    - Input image size: 384 x 384
+- **Dataset:** eu-common (707 classes)
+    - Intermediate training involved ~8000 species from all over the world
+
+- **Papers:**
+    - Three things everyone should know about Vision Transformers: <https://arxiv.org/abs/2203.09795>
+    - data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language: <https://arxiv.org/abs/2202.03555>
+
+## Model Usage
+
+### Image Classification
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-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, 707), representing class probabilities.
+```
+
+### Image Embeddings
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-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, 384)
+```
+
+### Detection Feature Map
+
+```python
+from PIL import Image
+import birder
+
+(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-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:
+# [('neck', torch.Size([1, 384, 24, 24]))]
+```
+
+## Citation
+
+```bibtex
+@misc{touvron2022thingsknowvisiontransformers,
+      title={Three things everyone should know about Vision Transformers},
+      author={Hugo Touvron and Matthieu Cord and Alaaeldin El-Nouby and Jakob Verbeek and Hervé Jégou},
+      year={2022},
+      eprint={2203.09795},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2203.09795},
+}
+
+@misc{https://doi.org/10.48550/arxiv.2202.03555,
+      title={data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
+      author={Alexei Baevski and Wei-Ning Hsu and Qiantong Xu and Arun Babu and Jiatao Gu and Michael Auli},
+      year={2022},
+      eprint={2202.03555},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2202.03555},
+}
+```