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mlx-examples/cifar
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dmdaksh 7d7e236061
- Removed unused Python imports (#683) 
- bert/model.py:10: tree_unflatten
  - bert/model.py:2: dataclass
  - bert/model.py:8: numpy
  - cifar/resnet.py:6: Any
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  - llms/tests/test_lora.py:13: yaml_loader
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2024-04-16 07:50:32 -07:00
..
dataset.py Update a few examples to use compile (#420) 2024-02-08 13:00:41 -08:00
main.py Update a few examples to use compile (#420) 2024-02-08 13:00:41 -08:00
README.md Updated CIFAR-10 ResNet example to use BatchNorm instead of LayerNorm (#257) 2024-01-12 05:43:11 -08:00
requirements.txt Update a few examples to use compile (#420) 2024-02-08 13:00:41 -08:00
resnet.py - Removed unused Python imports (#683) 2024-04-16 07:50:32 -07:00

README.md

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.