Added Finetuned model.

README.md

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+# CLIP ViT Base Patch32 Fine-Tuned on PatchCamelyon (PCAM)
+
+## Overview
+This repository contains a fine-tuned version of the [CLIP ViT Base Patch32](https://huggingface.co/tanganke/clip-vit-base-patch32_pcam) model on the [PatchCamelyon (PCAM)](https://huggingface.co/datasets/1aurent/PatchCamelyon) dataset. The model is optimized for histopathological image classification.
+
+## Model Details
+- **Base Model**: CLIP ViT Base Patch32
+- **Dataset**: PatchCamelyon (PCAM)
+- **Optimizer**: AdamW
+- **Loss Function**: Cross-Entropy Loss
+- **Batch Size**: 32
+- **Hardware**: Trained on GPU
+
+## Training Performance
+- **Epoch 1 Results:**
+  - **Train Loss**: 0.1520
+  - **Train Accuracy**: 94.35%
+  - **Validation Accuracy**: 95.16%
+
+## Usage
+### Installation
+Ensure you have `transformers`, `torch`, and `safetensors` installed:
+```bash
+pip install transformers torch safetensors
+```
+
+### Loading the Model
+```python
+from transformers import CLIPProcessor, CLIPModel
+import torch
+
+model_path = "lens-ai/clip-vit-base-patch32_pcam_finetuned"
+model = CLIPModel.from_pretrained(model_path)
+processor = CLIPProcessor.from_pretrained(model_path)
+```
+
+### Running Inference
+```python
+from PIL import Image
+
+image = Image.open("sample_image.png")
+inputs = processor(images=image, return_tensors="pt")
+outputs = model.get_image_features(**inputs)
+```
+
+## Evaluation
+We plan to release additional metrics, including robustness evaluation with adversarial attacks in future updates.
+
+## License
+This model is released under the MIT License.
+
+## Contact
+For any questions, please reach out to **Venkata Tej** at [LensAI](https://huggingface.co/lens-ai).
+
+

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model.safetensors

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