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* replaced nn.LayerNorm by nn.BatchNorm * mlx>=0.0.8 required * updated default to 30 epochs instead of 100 * updated README after adding BatchNorm * requires mlx>=0.0.9 * updated README.md with results for mlx-0.0.9
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CIFAR and ResNets
An example of training a ResNet on CIFAR-10 with MLX. Several ResNet configurations in accordance with the original paper are available. The example also illustrates how to use MLX Data to load the dataset.
Pre-requisites
Install the dependencies:
pip install -r requirements.txt
Running the example
Run the example with:
python main.py
By default the example runs on the GPU. To run on the CPU, use:
python main.py --cpu
For all available options, run:
python main.py --help
Results
After training with the default resnet20 architecture for 30 epochs, you
should see the following results:
Epoch: 29 | avg. Train loss 0.294 | avg. Train acc 0.897 | Throughput: 270.81 images/sec
Epoch: 29 | Test acc 0.841
Note this was run on an M1 Macbook Pro with 16GB RAM.
At the time of writing, mlx doesn't have built-in learning rate schedules.
We intend to update this example once these features are added.