| 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 | |