---
tags:
- image-classification
- timm
library_name: timm
license: mit
datasets:
- cifar10
metrics:
- accuracy
model-index:
  - name: resnet18
    results: 
      - task: 
          type: image-classification
        dataset:
          name: cifar10
          type: cifar10
        metrics:
          - name: accuracy
            type: accuracy
            value: 94.73
---
# Model card for resnet18_cifar10

This is a resnet18 model trained on the cifar10 dataset.
To load this model use the `timm` library and run the following code:
```python
import timm
model = timm.create_model("hf_hub:SamAdamDay/resnet18_cifar10", pretrained=True)
```

The model was trained using the following command:
```bash
./distributed_train.sh --dataset torch/cifar10 --data-dir /root/data --dataset-download --model resnet18 --lr-base 0.3 --epochs 100 --input-size 3 256 256 -mean 0.49139968 0.48215827 0.44653124 --std 0.24703233 0.24348505 0.26158768 --num-classes 10
```

## Metrics

The model has a test accuracy of 94.73.

## Model Details
- **Dataset:** cifar10
- **Number of epochs:** 100
- **Batch size:** 128
- **Base LR:** 0.3
- **LR scheduler:** cosine
- **Input size** (3, 256, 256), images are scaled to this size 
- **PyTorch version:** 2.3.0+cu121
- **timm version:** 1.0.7