Update README.md

README.md

@@ -1,8 +1,69 @@
 ---
 tags:
-- image-classification
 - timm
+- feature-extraction
+- image-classification
 library_name: timm
 license: apache-2.0
 ---
+
 # Model card for vit_giant_patch14_224.dinobloom
+
+![](https://github.com/marrlab/DinoBloom/blob/9ea2f950e1f016cd7f899b3ed025d12b6a355d9f/media/overview.png?raw=true)
+
+## Model Details
+
+- **Model Type:** Feature backbone
+- **Model Stats:**
+    - Params: 1136M (giant)
+  - Image size: 224 x 224 x 3
+  - Patch size: 14 x 14 x 3
+- **Repository:** [github.com:marrlab/DinoBloom](https://github.com/marrlab/DinoBloom)
+- **Original Weights:** [Zenodo](https://zenodo.org/records/10908163)
+- **License:** [Apache License 2.0](https://github.com/marrlab/DinoBloom/blob/main/LICENSE)
+- **Papers:**
+  - [DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology](https://arxiv.org/abs/2404.05022)
+
+## Model Usage
+
+### Image Embeddings
+
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+# get example histology image
+img = Image.open(
+  urlopen(
+    "https://raw.githubusercontent.com/zxaoyou/segmentation_WBC/master/Dataset%201/001.bmp"
+  )
+)
+
+# load model from the hub
+model = timm.create_model(
+  model_name="hf-hub:1aurent/vit_giant_patch14_224.dinobloom",
+  pretrained=True,
+).eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
+output = model(data)  # output is a (batch_size, num_features) shaped tensor
+```
+
+
+## Citation
+
+```bibtex
+@misc{koch2024dinobloom,
+  title         = {DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology}, 
+  author        = {Valentin Koch and Sophia J. Wagner and Salome Kazeminia and Ece Sancar and Matthias Hehr and Julia Schnabel and Tingying Peng and Carsten Marr},
+  year          = {2024},
+  eprint        = {2404.05022},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV}
+}
+```