MNIST Model Trained on Modal
This is a PyTorch model trained on the MNIST dataset using an NVIDIA A100 GPU on Modal.
Model Details
- Architecture: Simple CNN (Conv2D -> Conv2D -> MaxPool -> Dropout -> FC -> Dropout -> FC)
- Dataset: MNIST
- Final Test Accuracy: 99.02%
Links
- W&B Run: View training on Weights & Biases
- Training Script: The script used to train this model is available in the repository as
train_mnist.py.
Training Configuration
- Batch Size: 64
- Learning Rate: 1.0 (Adadelta)
- Epochs: 5
Training Metrics
| Epoch | Train Loss | Test Loss | Accuracy |
|---|---|---|---|
| 1 | 0.2036 | 0.0441 | 98.40% |
| 2 | 0.0828 | 0.0376 | 98.84% |
| 3 | 0.0622 | 0.0487 | 98.38% |
| 4 | 0.0528 | 0.0301 | 99.05% |
| 5 | 0.0466 | 0.0314 | 99.02% |
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support