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Tanishrajput/
Inception-v1

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Inception-v1 (GoogLeNet) CIFAR-10

This repository contains the Inception-v1 (GoogLeNet) model trained on the CIFAR-10 dataset using PyTorch.
It achieves 91.21% test accuracy and is ready for inference or fine-tuning.

I hope you find this model useful and easy to integrate into your projects.

Model Description

Architecture: Inception-v1 (GoogLeNet) with auxiliary classifiers
Dataset: CIFAR-10 (10 classes, 60,000 32x32 color images)
Performance: 91.21% test accuracy
Framework: PyTorch
Input: 3x32x32 images (RGB)
Output: Class probabilities for 10 classes:
airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck

🔗 Links

GitHub Repository: CNN Models GitHub Repo
Streamlit: Link

Usage

Feel free to download and use the model in your own projects. Here's a simple example:

python

import torch
from googlenet_model import GoogLeNetCIFAR  # Make sure this contains the model implementation

# Load the pretrained model
model = GoogLeNetCIFAR(num_classes=10)
model.load_state_dict(torch.load("Inception-v1.pth", map_location=torch.device('cpu')))
model.eval()

# Example inference
from torchvision import transforms
from PIL import Image

transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

image = Image.open("example_image.png")
input_tensor = transform(image).unsqueeze(0)  # Add batch dimension

output, _, _ = model(input_tensor)  # Main output and auxiliary classifiers
predicted_class = output.argmax(1).item()
print("Predicted Class:", predicted_class)

Training Details

Optimizer: Adam with weight decay 5e-4
Learning Rate Scheduler: StepLR (step=15, gamma=0.1)
Loss Function: CrossEntropyLoss with auxiliary loss weighting
Weight Decay: 5e-4
Batch Size: 128
Epochs: 50
Data Augmentation: Random horizontal flip, normalization

License

This model is released under the MIT License.

Tanishrajput/Inception-v1