---
license: mit
language:
- en
library_name: pytorch
pipeline_tag: image-classification
tags:
- pytorch
- densenet
- cnn
- cifar10
- image-classification
- computer-vision
datasets:
- cifar10
---

# DenseNet on CIFAR-10

A PyTorch implementation of a DenseNet architecture trained from scratch on the CIFAR-10 dataset.

## Model Details

- Architecture: DenseNet
- Framework: PyTorch
- Dataset: CIFAR-10
- Input Size: 3 × 32 × 32
- Classes: 10
- Growth Rate: 32

## CIFAR-10 Classes

| Label | Class |
|------:|--------|
| 0 | airplane |
| 1 | automobile |
| 2 | bird |
| 3 | cat |
| 4 | deer |
| 5 | dog |
| 6 | frog |
| 7 | horse |
| 8 | ship |
| 9 | truck |

## Training

- Optimizer: SGD
- Learning Rate: 0.1
- Momentum: 0.9
- Weight Decay: 5e-4
- Scheduler: StepLR
- Loss: CrossEntropyLoss
- Epochs: 30
- Batch Size: 128

## Performance

| Metric | Value |
|--------|------:|
| Test Accuracy | 88.77% |
| Test Accuracy (DP) | 88.77% |

## Model Files

- `densenet_cifar10.pth`

## Load Model

```python
model = DenseNet()

model.load_state_dict(
    torch.load("densenet_cifar10.pth")
)

model.eval()
```

## Inference

```python
with torch.no_grad():
    outputs = model(images)
    _, predicted = torch.max(outputs, 1)
```

## Author

Ankit Bari

- GitHub: https://github.com/aijadugar
- Hugging Face: https://huggingface.co/aijadugar