24f2004275/cat-dog-classifier

Cat vs Dog Classifier

A PyTorch image classifier that distinguishes cats from dogs, trained on the Kaggle Cat and Dog dataset.

Models Included

File	Architecture	Val Accuracy	Description
mlp_classifier.pth	MLP	61.1%	Fully connected baseline
simple_cnn.pth	Simple CNN	72.6%	2-layer CNN from scratch
transfer_learning_fc_only.pth	ResNet18 (FC only)	97.6%	Frozen backbone, FC head
transfer_learning_layer4_fc.pth	ResNet18 (Layer4+FC)	98.1%	Partially unfrozen ResNet18
results.json	-	-	Training/validation metrics

Usage

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image

# Load model
model = models.resnet18()
model.fc = nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load("transfer_learning_layer4_fc.pth", weights_only=True))
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])

image = Image.open("cat.jpg")
tensor = transform(image).unsqueeze(0)

# Predict
with torch.no_grad():
    logits = model(tensor)
    probs = torch.softmax(logits, dim=1)
    predicted = torch.argmax(probs, dim=1).item()

classes = ["cat", "dog"]
print(f"Prediction: {classes[predicted]} ({probs[0][predicted]:.1%})")

Training Details

• Dataset: Kaggle Cat and Dog (8,007 train / 2,025 test)
• Input: 224x224 RGB, normalized to [-1, 1]
• Optimizer: Adam (lr=0.001)
• Loss: CrossEntropyLoss
• Epochs: MLP(3), CNN(5), Transfer Learning(3)

Results

Model	Train Acc	Val Acc	Train Loss	Val Loss
MLP	66.1%	61.1%	123.8	36.2
CNN	88.6%	70.4%	53.0	36.0
Transfer Learning (FC only)	96.5%	97.2%	18.1	3.7
Transfer Learning (Layer4+FC)	98.6%	98.1%	8.1	3.0