hp1318/alexnet-finetuned-herlev

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.

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Model Details

• Architecture: AlexNet
• Framework: PyTorch
• Input size: 224 × 224 RGB
• Number of classes: 7
• Task: Cervical cell image classification

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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

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How to Use

Installation

pip install torch torchvision huggingface_hub pillow

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Load the Model

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()

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Image Preprocessing

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]
    )
])

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Run Inference

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())

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Output

The model outputs logits for all cervical cell classes.
The predicted label corresponds to the class with the highest logit score.

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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.

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Citation

If you use this model in academic work, please cite the corresponding paper or repository.