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52 lines
1.1 KiB
Markdown
52 lines
1.1 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 100 epochs, you
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should see the following results:
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```
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Epoch: 99 | avg. Train loss 0.320 | avg. Train acc 0.888 | Throughput: 416.77 images/sec
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Epoch: 99 | Test acc 0.807
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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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or a `BatchNorm` layer. We intend to update this example once these features
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are added.
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