Update README.md

README.md

@@ -1,3 +1,51 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- vit
+- image-classification
+- beans
+- transfer-learning
+---
+
+# ViT Beans Model
+
+This model was fine-tuned using transfer learning on the ["beans"](https://huggingface.co/datasets/beans) dataset from the Hugging Face Datasets Hub.  
+It classifies bean plant leaves into the following categories:
+
+- `LABEL_0`: angular_leaf_spot  
+- `LABEL_1`: bean_rust  
+- `LABEL_2`: healthy
+
+## Model architecture
+
+The base model is `google/vit-base-patch16-224`.
+
+## Training
+
+Transfer learning was used with a ViT model pre-trained on ImageNet-21k.
+
+## Evaluation
+
+This model was compared to a zero-shot classification using CLIP (`openai/clip-vit-base-patch32`).
+
+### Zero-Shot Results on Oxford Pets (as required):
+
+- **Accuracy**: 0.9993189573287964
+- **Precision**: 0.5794700118713081
+- **Recall**: 0.10156987264053896
+- **Model used**: `openai/clip-vit-base-patch32`
+
+## Example
+
+```python
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import torch
+
+image = Image.open("example_input.png")
+extractor = ViTFeatureExtractor.from_pretrained("LindiSimon/vit-beans-model")
+inputs = extractor(images=image, return_tensors="pt")
+model = ViTForImageClassification.from_pretrained("LindiSimon/vit-beans-model")
+with torch.no_grad():
+    logits = model(**inputs).logits
+predicted_class = logits.argmax(-1).item()