Update README.md

README.md

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----
-license: mit
----
+---
+tags:
+- image-feature-extraction
+- cell representation
+- histology
+- medical imaging
+- self-supervised learning
+- vision transformer
+- foundation model
+license: mit
+---
+
+# Model card for LEMON
+
+`LEMON` is an open-source foundation model for single-cell histology images. The model is a Vision Transformer (ViT-s/8) trained using self-supervised learning on a dataset of 10 million histology cell images sampled from 10,000 slides from TCGA. 
+It is described in detail in its [OpenReview paper](https://openreview.net/pdf?id=JAalsmy7bZ).
+
+`LEMON` can be used to extract robust features from single-cell histology images for various downstream applications, such as gene expression prediction or cell type classification.
+
+
+## How to use it to extract features.
+
+The code below can be used to run inference. `LEMON` expects images of size 40x40 that were extracted at 0.25 microns per pixel (40X).
+
+```python
+import torch
+from pathlib import Path
+from torchvision.transforms import ToPILImage
+from model import prepare_transform, get_vit_feature_extractor
+
+device = "cpu"
+model_name = "vits8" 
+target_cell_size = 40 
+weight_path = Path("lemon.pth.tar")
+stats_path = Path("mean_std.json")
+
+# Model
+transform = prepare_transform(stats_path, size=target_cell_size)
+model = get_vit_feature_extractor(weight_path, model_name, img_size=target_cell_size)
+model.eval()
+model.to(device)
+
+# Data
+input = torch.rand(3, target_cell_size, target_cell_size)
+input = ToPILImage()(input)
+
+# Inference
+with torch.autocast(device_type=device, dtype=torch.float16):
+    with torch.inference_mode():
+        features = model(transform(input).unsqueeze(0).to(device))
+
+assert features.shape == (1, 384)
+```