metadata
license: apache-2.0
tags:
- image-classification
- medical-imaging
- cervical-cancer
- pap-smear
- pytorch
AlexNet Fine-Tuned on HERLEV Dataset
This repository provides a fine-tuned AlexNet model trained on the HERLEV cervical cytology dataset for multi-class cervical cell classification.
Model Details
- Architecture: AlexNet
- Framework: PyTorch
- Input size: 224x224 RGB
- Classes: 5
Classes
0: Superficial–Intermediate
1: Parabasal
2: Koilocytotic
3: Dyskeratotic
4: Metaplastic
How to Use
import torch
from PIL import Image
from torchvision import transforms
from model import load_model
device = "cuda" if torch.cuda.is_available() else "cpu"
model = load_model("best_alexnet.pth", device=device)
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]
)
])
img = Image.open("cell_image.jpg").convert("RGB")
img = transform(img).unsqueeze(0).to(device)
with torch.no_grad():
pred = model(img).argmax(1)
print("Prediction:", pred.item())
Intended Use
Research and educational purposes only.