Instructions to use edadaltocg/densenet121_cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use edadaltocg/densenet121_cifar100 with timm:
import timm model = timm.create_model("hf_hub:edadaltocg/densenet121_cifar100", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| language: en | |
| license: mit | |
| library_name: timm | |
| tags: | |
| - image-classification | |
| - densenet121 | |
| - cifar100 | |
| datasets: cifar100 | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: densenet121_cifar100 | |
| results: | |
| - task: | |
| type: image-classification | |
| dataset: | |
| name: CIFAR-100 | |
| type: cifar100 | |
| metrics: | |
| - type: accuracy | |
| value: 0.7619 | |
| # Model Card for Model ID | |
| This model is a small densenet121 trained on cifar100. | |
| - **Test Accuracy:** 0.7619 | |
| - **License:** MIT | |
| ## How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| ```python | |
| import detectors | |
| import timm | |
| model = timm.create_model("densenet121_cifar100", pretrained=True) | |
| ``` | |
| ## Training Data | |
| Training data is cifar100. | |
| ## Training Hyperparameters | |
| - **config**: `scripts/train_configs/cifar100.json` | |
| - **model**: `densenet121_cifar100` | |
| - **dataset**: `cifar100` | |
| - **batch_size**: `128` | |
| - **epochs**: `300` | |
| - **validation_frequency**: `5` | |
| - **seed**: `1` | |
| - **criterion**: `CrossEntropyLoss` | |
| - **criterion_kwargs**: `{}` | |
| - **optimizer**: `SGD` | |
| - **lr**: `0.1` | |
| - **optimizer_kwargs**: `{'momentum': 0.9, 'weight_decay': 0.0005}` | |
| - **scheduler**: `CosineAnnealingLR` | |
| - **scheduler_kwargs**: `{'T_max': 280}` | |
| - **debug**: `False` | |
| ## Testing Data | |
| Testing data is cifar100. | |
| --- | |
| This model card was created by Eduardo Dadalto. |