https://github.com/Aavache/mlx-resnet

ResNet implementation with the MLX, Apple's DL framework.
https://github.com/Aavache/mlx-resnet

apple computer-vision deep-learning machine-learning mlx mnist resnet

Last synced: 10 days ago
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ResNet implementation with the MLX, Apple's DL framework.

• Host: GitHub
• URL: https://github.com/Aavache/mlx-resnet
• Owner: Aavache
• Created: 2023-12-09T18:41:12.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2023-12-09T18:44:37.000Z (11 months ago)
• Last Synced: 2023-12-09T20:36:51.432Z (11 months ago)
• Topics: apple, computer-vision, deep-learning, machine-learning, mlx, mnist, resnet
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
🍏 MLX - ResNet 🍏


ResNet implementation with the [MLX](https://github.com/ml-explore/mlx), Apple's deep learning framework.

Drop a ⭐️!!

## MNIST with ResNet18

Here the logs when training with ResNet18 (no tuning at all) for couple epochs:

```sh

Epoch: 0 | Iter: 0 | Loss: 2.550

Epoch: 0 | Iter: 50 | Loss: 1.813

Epoch: 0 | Iter: 100 | Loss: 1.241

Epoch: 0 | Iter: 150 | Loss: 0.965

Epoch: 0 | Iter: 200 | Loss: 0.741

Epoch 0: Test accuracy 0.671

Epoch: 1 | Iter: 0 | Loss: 1.039

Epoch: 1 | Iter: 50 | Loss: 0.491

Epoch: 1 | Iter: 100 | Loss: 0.471

Epoch: 1 | Iter: 150 | Loss: 0.497

Epoch: 1 | Iter: 200 | Loss: 0.377

Epoch 1: Test accuracy 0.876

Epoch: 2 | Iter: 0 | Loss: 0.425

Epoch: 2 | Iter: 50 | Loss: 0.367

Epoch: 2 | Iter: 100 | Loss: 0.334

Epoch: 2 | Iter: 150 | Loss: 0.323

Epoch: 2 | Iter: 200 | Loss: 0.346

Epoch 2: Test accuracy 0.916

Epoch: 3 | Iter: 0 | Loss: 0.334

Epoch: 3 | Iter: 50 | Loss: 0.308

Epoch: 3 | Iter: 100 | Loss: 0.282

Epoch: 3 | Iter: 150 | Loss: 0.230

Epoch: 3 | Iter: 200 | Loss: 0.173

Epoch 3: Test accuracy 0.902

Epoch: 4 | Iter: 0 | Loss: 0.282

Epoch: 4 | Iter: 50 | Loss: 0.184

Epoch: 4 | Iter: 100 | Loss: 0.268

Epoch: 4 | Iter: 150 | Loss: 0.205

Epoch: 4 | Iter: 200 | Loss: 0.246

Epoch 4: Test accuracy 0.932

```

## Future updates

* `dilation` and `groups` features in convolutional layers is missing ([issue](https://github.com/ml-explore/mlx/issues/100) created). More recent ResNet requires this parameters.

* Batch norm is also missing.

* No pretrained weights.

## Contribute

Feel free to create issues and PRs :)