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2b61d9deb6
* 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
51 lines
1.0 KiB
Markdown
51 lines
1.0 KiB
Markdown
# CIFAR and ResNets
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An example of training a ResNet on CIFAR-10 with MLX. Several ResNet
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configurations in accordance with the original
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[paper](https://arxiv.org/abs/1512.03385) are available. The example also
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illustrates how to use [MLX Data](https://github.com/ml-explore/mlx-data) to
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load the dataset.
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## Pre-requisites
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Install the dependencies:
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```
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pip install -r requirements.txt
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```
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## Running the example
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Run the example with:
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```
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python main.py
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```
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By default the example runs on the GPU. To run on the CPU, use:
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```
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python main.py --cpu
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```
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For all available options, run:
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```
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python main.py --help
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```
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## Results
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After training with the default `resnet20` architecture for 30 epochs, you
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should see the following results:
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```
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Epoch: 29 | avg. Train loss 0.294 | avg. Train acc 0.897 | Throughput: 270.81 images/sec
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Epoch: 29 | Test acc 0.841
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```
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Note this was run on an M1 Macbook Pro with 16GB RAM.
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At the time of writing, `mlx` doesn't have built-in learning rate schedules.
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We intend to update this example once these features are added.
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