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Tanishrajput/
ResNet-50

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ResNet-50 CIFAR-100

This repository contains the ResNet-50 model trained on the CIFAR-100 dataset using PyTorch.
It achieves 87.16% 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: ResNet-50 with residual connections
Dataset: CIFAR-100 (100 classes, 60,000 32x32 color images)
Performance: 87.16% test accuracy
Framework: PyTorch
Input: 3x32x32 images (RGB)
Output: Class probabilities for 100 classes

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

# Load the pretrained model
model = models.resnet50()
model.fc = nn.Linear(model.fc.in_features, 100)  # CIFAR-100 has 100 classes
model.load_state_dict(torch.load("ResNet-50.pth", map_location=torch.device('cpu')))
model.eval()

# Example inference
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)
predicted_class = output.argmax(1).item()
print("Predicted Class:", predicted_class)

Training Details

Optimizer: SGD with momentum
Learning Rate Scheduler: Cosine Annealing
Loss Function: CrossEntropyLoss
Weight Decay: 5e-4
Batch Size: 128
Epochs: 50
Data Augmentation: Random horizontal flip, random crop, CutMix, Mixup, normalization
Hardware: Trained on 2× NVIDIA T4 GPUs with nn.DataParallel

License

This model is released under the MIT License.

Tanishrajput/ResNet-50