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README.md
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---
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language: en
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license: mit
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library_name: timm
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tags:
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- image-classification
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- resnet34
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- cifar100
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datasets: cifar100
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metrics:
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- accuracy
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model-index:
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- name: resnet34_cifar100
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results:
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- task:
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type: image-classification
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dataset:
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name: CIFAR-100
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type: cifar100
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metrics:
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- type: accuracy
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value: 0.7978
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---
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# Model Card for Model ID
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This model is a small resnet34 trained on cifar100.
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- **Test Accuracy:** 0.7978
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- **License:** MIT
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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import detectors
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import timm
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model = timm.create_model("resnet34_cifar100", pretrained=True)
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```
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## Training Data
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Training data is cifar100.
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## Training Hyperparameters
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- **config**: `scripts/train_configs/cifar100.json`
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- **model**: `resnet34_cifar100`
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- **dataset**: `cifar100`
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- **batch_size**: `128`
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- **epochs**: `300`
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- **validation_frequency**: `5`
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- **seed**: `1`
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- **criterion**: `CrossEntropyLoss`
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- **criterion_kwargs**: `{}`
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- **optimizer**: `SGD`
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- **lr**: `0.1`
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- **optimizer_kwargs**: `{'momentum': 0.9, 'weight_decay': 0.0005}`
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- **scheduler**: `CosineAnnealingLR`
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- **scheduler_kwargs**: `{'T_max': 280}`
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- **debug**: `False`
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## Testing Data
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Testing data is cifar100.
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---
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This model card was created by Eduardo Dadalto.
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