README.md

metadata

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: 92.73

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:

import timm
model = timm.create_model("hf_hub:SamAdamDay/resnet18_cifar10", pretrained=True)

The model was trained using the following command:

./distributed_train.sh --dataset torch/cifar10 --data-dir /root/data --dataset-download --model resnet18 --lr-base 0.3

Metrics

The model has a test accuracy of 92.73.

Model Details

• Dataset: cifar10
• Number of epochs: 300
• Batch size: 128
• Base LR: 0.3
• LR scheduler: cosine
• PyTorch version: 2.3.0+cu121
• timm version: 1.0.7