---
tags:
- image-classification
- imagenette
- resnet50-pytorch
- pytorch
datasets:
- imagenette
metrics:
- accuracy
---

# ImageNette resnet50-pytorch

## Model Description

resnet50-pytorch trained on 10-class ImageNet subset (ImageNette) with advanced augmentation techniques.

### Model Architecture
- **Architecture**: resnet50-pytorch
- **Dataset**: ImageNette
- **Classes**: 10

### Training Configuration
- **Batch Size**: 128
- **Optimizer**: sgd (momentum=0.9, weight_decay=1e-3)
- **Scheduler**: onecycle
- **Augmentation**: HorizontalFlip, ShiftScaleRotate, Cutout, ColorJitter
- **MixUp**: Alpha=0.2
- **Label Smoothing**: 0.1
- **Mixed Precision**: True
- **Gradient Clipping**: 1.0

### Performance
- **Best Test Accuracy**: 24.28%
- **Total Epochs Trained**: 2
- **Final Train Accuracy**: 16.16%
- **Final Test Accuracy**: 24.28%

### Training History
- **Best Epoch**: 2
- **Train Loss**: 2.4702 → 2.3422
- **Test Loss**: 2.6904 → 2.1273

### Usage

```python
import torch
from huggingface_hub import hf_hub_download

# Download model
checkpoint_path = hf_hub_download(
    repo_id="pandurangpatil/imagenet10trial",
    filename="best_model.pth"
)

# Load checkpoint
checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)

# Load model (you'll need to have the model definition)
# from models import get_model
# model = get_model('resnet50-pytorch', num_classes=10)
# model.load_state_dict(checkpoint['model_state_dict'])
# model.eval()
```

### Training Details
- **Dataset**: ImageNette (9469 train, 3925 test)
- **Classes**: 10
- **Image Size**: 160×160 or 224×224
- **Normalization**: mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)

### Files
- `best_model.pth` - Best performing model checkpoint
- `training_curves.png` - Training/test accuracy and loss curves
- `lr_finder_plot.png` - Learning rate finder results
- `metrics.json` - Complete training history
- `config.json` - Hyperparameter configuration

### License
MIT

### Citation
```bibtex
@misc{resnet50-pytorch-imagenette,
  title = {ImageNette resnet50-pytorch},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/pandurangpatil/imagenet10trial}
}
```