| language: en | |
| license: mit | |
| library_name: timm | |
| tags: | |
| - image-classification | |
| - resnet18 | |
| - cifar100 | |
| datasets: cifar100 | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: resnet18_cifar100 | |
| results: | |
| - task: | |
| type: image-classification | |
| dataset: | |
| name: CIFAR-100 | |
| type: cifar100 | |
| metrics: | |
| - type: accuracy | |
| value: 0.7843 | |
| # Model Card for Model ID | |
| This model is a small resnet18 trained on cifar100. It achieves the following results on the evaluation set: Accuracy: 0.7843. | |
| - **Developed by:** Eduardo Dadalto | |
| - **License:** MIT | |
| ## How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| ```python | |
| import timm | |
| model = timm.create_model("{model_name}", pretrained=True) | |
| ``` | |
| ## Training Data | |
| <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> | |
| Training data is cifar100. | |
| ## Training Hyperparameters | |
| ## Evaluation | |
| ### Testing Data | |
| <!-- This should link to a Data Card if possible. --> | |
| Testing data is cifar100. | |
| ### Metrics | |
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> | |
| Accuracy | |
| ## Results | |
| Accuracy is 0.7843. |