PyTorch implementation of Mixer-nano (#parameters is 0.67M, originally Mixer-S/16 has 18M) with 90.83 % acc. on CIFAR-10. Training from scratch.
- Python 3.9.6
- PyTorch 1.10.0
- Weights and Biases account for logging experiments.
$git clone https://github.com/omihub777/MLP-Mixer-CIFAR.git
$cd MLP-Mixer-CIFAR
$bash setup.sh
$main.py --dataset c10 --model mlp_mixer --autoaugment --cutmix-prob 0.5| Dataset | Acc.(%) | Time(hh:mm:ss) | Steps |
|---|---|---|---|
| CIFAR-10 | 90.83% | 3:34.31 | 117.3k |
| CIFAR-100 | 67.51% | 3:35.26 | 117.3k |
| SVHN | 97.63% | 5:23.26 | 171.9k |
- Number of Parameters: 0.67M
- Device: P100 (single GPU)
- Accuracy
- Accuracy
- Accuracy
| Param | Value |
|---|---|
| Adam beta1 | 0.9 |
| Adam beta2 | 0.99 |
| AutoAugment | True |
| Batch Size | 128 |
| CutMix prob. | 0.5 |
| CutMix beta | 1.0 |
| Dropout | 0.0 |
| Epoch | 300 |
| Hidden_C | 512 |
| Hidden_S | 64 |
| Hidden | 128 |
| (Init LR, Last LR) | (1e-3, 1e-6) |
| Label Smoothing | 0.1 |
| Layers | 8 |
| LR Scheduler | Cosine |
| Optimizer | Adam |
| Random Seed | 3407 |
| Weight Decay | 5e-5 |
| Warmup | 5 epochs |
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