Update README.md

README.md

@@ -6,7 +6,125 @@ tags:
 - pytorch_model_hub_mixin
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: https://huggingface.co/hp1318/alexnet-finetuned-herlev
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+---
+license: apache-2.0
+pipeline_tag: image-classification
+tags:
+- medical-imaging
+- cervical-cancer
+- pap-smear
+- pytorch
+- alexnet
+---
+
+# AlexNet Fine-Tuned on HERLEV Dataset
+
+This repository contains a fine-tuned **AlexNet** model trained on the **HERLEV cervical cytology dataset** for multi-class classification of Pap smear images.
+
+The model was uploaded using the **PyTorchModelHubMixin**, enabling native Hugging Face loading via `from_pretrained`.
+
+---
+
+## Model Details
+
+- **Architecture:** AlexNet  
+- **Framework:** PyTorch  
+- **Input size:** 224 × 224 RGB  
+- **Number of classes:** 7  
+- **Task:** Cervical cell image classification  
+
+---
+
+## Classes
+
+| Label | Cell Type |
+|------|-----------|
+| 0 | Superficial Squamous |
+| 1 | Intermediate Squamous |
+| 2 | Columnar |
+| 3 | Mild Dysplasia |
+| 4 | Moderate Dysplasia |
+| 5 | Severe Dysplasia |
+| 6 | Carcinoma in situ |
+
+---
+
+## How to Use
+
+### Installation
+
+
+```bash
+pip install torch torchvision huggingface_hub pillow
+```
+
+---
+
+## Load the Model
+
+```python
+import torch
+from huggingface_hub import PyTorchModelHubMixin
+from torchvision import transforms
+from PIL import Image
+
+from model import AlexNetHERLEV
+
+model = AlexNetHERLEV.from_pretrained(
+    "hp1318/alexnet-finetuned-herlev"
+)
+
+model.eval()
+```
+
+---
+
+## Image Preprocessing
+
+```python
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+```
+
+---
+
+## Run Inference
+
+```python
+image = Image.open("cell_image.jpg").convert("RGB")
+image = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    outputs = model(image)
+    prediction = torch.argmax(outputs, dim=1)
+
+print("Predicted class:", prediction.item())
+```
+
+---
+
+## Output
+
+The model outputs logits for all cervical cell classes.  
+The predicted label corresponds to the class with the highest logit score.
+
+---
+
+## Notes
+
+- Input images must be RGB format.
+- Images are resized to 224 × 224 before inference.
+- Normalization follows ImageNet statistics.
+- This model is intended for research and educational use only.
+
+---
+
+## Citation
+
+If you use this model in academic work, please cite the corresponding paper or repository.