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---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet18
- cifar10
datasets: cifar10
metrics:
- accuracy
model-index:
- name: resnet18_cifar10
results:
- task:
type: image-classification
dataset:
name: CIFAR-10
type: cifar10
metrics:
- type: accuracy
value: 0.9498
---
# Model Card for Model ID
This model is a small resnet18 trained on cifar10.
- **Test Accuracy:** 0.9498
- **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("resnet18_cifar10", pretrained=True)
```
## Training Data
Training data is cifar10.
## Training Hyperparameters
- **config**: `scripts/train_configs/cifar10.json`
- **model**: `resnet18_cifar10`
- **dataset**: `cifar10`
- **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, 'nesterov': 'True'}`
- **scheduler**: `ReduceLROnPlateau`
- **scheduler_kwargs**: `{'factor': 0.1, 'patience': 3, 'threshold': 0.001, 'mode': 'max'}`
- **debug**: `False`
## Testing Data
Testing data is cifar10.
---
This model card was created by Eduardo Dadalto. |