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: 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:

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 --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