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 hiera_abswin_base_mim
+
+A Hiera with absolute window position embedding strategy image encoder pre-trained using Masked Image Modeling (MIM). This model has *not* been fine-tuned for a specific classification task and is intended to be used as a general-purpose feature extractor or a backbone for downstream tasks like object detection, segmentation, or custom classification.
+
+## Model Details
+
+- **Model Type:** Image encoder and detection backbone
+- **Model Stats:**
+    - Params (M): 50.5
+    - Input image size: 224 x 224
+- **Dataset:** Trained on a diverse dataset of approximately 12M images, including:
+    - iNaturalist 2021 (~3.3M)
+    - WebVision-2.0 (~1.5M random subset)
+    - imagenet-w21-webp-wds (~1M random subset)
+    - SA-1B (~220K random subset of 20 chunks)
+    - COCO (~120K)
+    - NABirds (~48K)
+    - GLDv2 (~40K random subset of 6 chunks)
+    - Birdsnap v1.1 (~44K)
+    - CUB-200 2011 (~18K)
+    - The Birder dataset (~6M, private dataset)
+
+- **Papers:**
+    - Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles: <https://arxiv.org/abs/2306.00989>
+    - Window Attention is Bugged: How not to Interpolate Position Embeddings: <https://arxiv.org/abs/2311.05613>
+
+## Model Usage
+
+### Image Embeddings
+
+```python
+import birder
+from birder.inference.classification import infer_image
+
+(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim", 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, 768)
+```
+
+### Detection Feature Map
+
+```python
+from PIL import Image
+import birder
+
+(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim", 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, 96, 56, 56])),
+#  ('stage2', torch.Size([1, 192, 28, 28])),
+#  ('stage3', torch.Size([1, 384, 14, 14])),
+#  ('stage4', torch.Size([1, 768, 7, 7]))]
+```
+
+## Citation
+
+```bibtex
+@misc{ryali2023hierahierarchicalvisiontransformer,
+      title={Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles},
+      author={Chaitanya Ryali and Yuan-Ting Hu and Daniel Bolya and Chen Wei and Haoqi Fan and Po-Yao Huang and Vaibhav Aggarwal and Arkabandhu Chowdhury and Omid Poursaeed and Judy Hoffman and Jitendra Malik and Yanghao Li and Christoph Feichtenhofer},
+      year={2023},
+      eprint={2306.00989},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2306.00989},
+}
+
+@misc{bolya2023windowattentionbuggedinterpolate,
+      title={Window Attention is Bugged: How not to Interpolate Position Embeddings},
+      author={Daniel Bolya and Chaitanya Ryali and Judy Hoffman and Christoph Feichtenhofer},
+      year={2023},
+      eprint={2311.05613},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2311.05613},
+}
+```