ResNet50 CIFAR-10 Model
| Property | Value |
|---|---|
| License | MIT |
| Framework | PyTorch (timm) |
| Dataset | CIFAR-10 |
| Accuracy | 94.65% |
What is resnet50_cifar10?
ResNet50 CIFAR-10 is a deep learning model that adapts the powerful ResNet50 architecture for image classification on the CIFAR-10 dataset. Created by edadaltocg, this implementation achieves impressive accuracy while maintaining efficiency and ease of use through the timm library.
Implementation Details
The model is trained using SGD optimizer with momentum (0.9) and weight decay (0.0005). Training runs for 300 epochs with a batch size of 128, utilizing ReduceLROnPlateau scheduler for learning rate adjustment. The initial learning rate is set to 0.1.
- Optimizer: SGD with Nesterov momentum
- Loss Function: CrossEntropyLoss
- Learning Rate Schedule: ReduceLROnPlateau with 0.1 factor and 3 epochs patience
- Validation Frequency: Every 5 epochs
Core Capabilities
- High-accuracy image classification on CIFAR-10 dataset
- Easy integration through timm library
- Optimized for 10-class classification tasks
- Robust performance with 94.65% accuracy
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized performance on CIFAR-10, achieving 94.65% accuracy through careful hyperparameter tuning and training strategy. It provides a great balance between accuracy and usability.
Q: What are the recommended use cases?
The model is ideal for CIFAR-10 image classification tasks, educational purposes, and as a baseline for transfer learning projects. It's particularly suitable for applications requiring reliable image classification on similar small-scale datasets.
