Apppublic

Koti05/Cat-Dog-Pandas

sourceHugging Faceupdated 11mo agoView on Hugging Face

0likes

App README

🐾 Cat-Dog-Pandas Classifier

A Streamlit web app that uses PyTorch and transfer learning (AlexNet) to classify images of Cats 🐱, Dogs 🐶, and Pandas 🐼 in real time. Deployed using Docker on Hugging Face Spaces.

🧠 Model Overview

—Base Model: AlexNet (Transfer Learning)
—Fine-tuned Layers: Layer4 + Fully Connected layers
—Custom Classifier:
—Linear(2048 → 512) + ReLU + Dropout(0.7)
—Linear(512 → 128) + ReLU + Dropout(0.3)
—Linear(128 → 3) [Output Layer]
—Classes: Cat, Dog, Panda
—Auto-Download: Model (model.pth) fetched automatically from Hugging Face
—Device Support: CPU/GPU auto-detection

📊 Performance Summary

Metric	Cat	Dog	Panda	Macro Avg	Weighted Avg
Precision	98.51%	99.49%	100.00%	99.33%	99.33%
Recall	99.50%	98.50%	100.00%	99.33%	99.33%
F1-Score	99.00%	98.99%	100.00%	99.33%	99.33%
Test Accuracy	–	–	–	99.33%	–

📁 Project Structure

Cat-Dog-Pandas/ ├── 🐳 Dockerfile ├── 📱 app.py ├── 📈 metrics.json ├── 🧠 model.pth ├── 📓 Project.ipynb ├── 📋 requirements.txt ├── 🖼️ confusion_matrix.png └── 🔧 datasplit.py

⚙️ Dockerfile (Used in this Project)

dockerfile

FROM python:3.9-slim

WORKDIR /app

RUN apt-get update && apt-get install -y \
    build-essential \
    curl \
    git \
    && rm -rf /var/lib/apt/lists/*

COPY requirements.txt .

RUN pip3 install --no-cache-dir -r requirements.txt

COPY . .

RUN useradd -m -u 1000 user && \
    chown -R user:user /app
USER user

EXPOSE 7860

HEALTHCHECK CMD curl --fail http://localhost:7860/_stcore/health

CMD ["streamlit","run","app.py","--server.port=7860","--server.address=0.0.0.0","--server.enableXsrfProtection=false","--server.enableCORS=false","--server.maxUploadSize=50"]
---