This repository is for training MobileNets on CIFAR100 using pytorch and Docker-based DL development environment
- Model codes are based on torchvision.
- Block argument parser is based on EfficientNet
You have to clone this repository recursively
git clone --recursive https://github.com/NoUnique/MobileNet-CIFAR100.pytorch.git
To build Docker image
./docker/compose -b
To run docker container for DL development
./docker/compose -r
To attach the container
./docker/compose -s
You must run code below in the container
To train MobileNetV2 on CIFAR-100 dataset with a single-GPU:
CUDA_VISIBLE_DEVICES=0 python train.py --flagfile configs/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf
To train MobileNetV2 on CIFAR-100 dataset with 4 GPUs:
horovodrun -np 4 python train.py --flagfile configs/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf
To train MobileNetV2 with custom block_args on CIFAR-100 dataset with 4 GPUs:
horovodrun -np 4 python train.py --flagfile configs/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf --BLOCK_ARGS=wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1
To test trained model(last checkpoint) on CIFAR-100 dataset with a single-GPU:
CUDA_VISIBLE_DEVICES=0 python test.py --flagfile configs/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf
To test trained model(specific checkpoint) on CIFAR-100 dataset with a single-GPU:
CUDA_VISIBLE_DEVICES=0 python test.py --flagfile configs/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf --PRETRAINED_CHECKPOINT_PATH=checkpoints/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1/checkpoint-200.pth.tar
If you test other block_args setting, you have to specify 'BLOCK_ARGS' flag
- default: MobileNetV2 with stride 1(stem), 1, 1, 2, 2, 1, 2, 1, other args are same to paper
CUDA_VISIBLE_DEVICES=0 python test.py --flagfile configs/train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf --BLOCK_ARGS=wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1
To run tensorboard service to 6006 port
./docker/compose --tensorboard
- validation accuracy is calculated during training via horovod(it may not correct)
| network | top1-acc | val-acc | MACs(M) | params(M) | ngpus | config | block_args |
|---|---|---|---|---|---|---|---|
| SEMobileNetV2 | 76.64 | (77.9) | 309.84 | 16.05 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm2.0_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c40_n2_s2,t4_c80_n3_s2,t4_c112_n3_s1,t4_c192_n4_s2,t4_c320_n1_s1 |
| SEMobileNetV2 | 75.92 | (77.6) | 118.90 | 7.16 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c40_n2_s2,t6_c80_n3_s2,t6_c112_n3_s1,t6_c192_n4_s2,t6_c320_n1_s1 |
| SEMobileNetV2 | 75.61 | (77.1) | 183.84 | 9.13 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.5_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c40_n2_s2,t4_c80_n3_s2,t4_c112_n3_s1,t4_c192_n4_s2,t4_c320_n1_s1 |
| SEMobileNetV2 | 75.10 | (76.6) | 94.29 | 4.61 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| SEMobileNetV2 | 74.71 | (76.2) | 81.57 | 4.32 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.75_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c40_n2_s2,t6_c80_n3_s2,t6_c112_n3_s1,t6_c192_n4_s2,t6_c320_n1_s1 |
| SEMobileNetV2 | 74.70 | (76.4) | 81.39 | 4.12 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c40_n2_s2,t4_c80_n3_s2,t4_c112_n3_s1,t4_c192_n4_s2,t4_c320_n1_s1 |
| SEMobileNetV2 | 74.63 | (75.9) | 81.83 | 4.61 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s2,t6_c32_n3_s1,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| SEMobileNetV2 | 74.52 | (76.3) | 64.11 | 2.76 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.75_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| SEMobileNetV2 | 73.44 | (75.2) | 56.13 | 2.54 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.75_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c40_n2_s2,t4_c80_n3_s2,t4_c112_n3_s1,t4_c192_n4_s2,t4_c320_n1_s1 |
| SEMobileNetV2 | 73.09 | (74.7) | 44.70 | 1.71 | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.75_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c32_n3_s2,t4_c64_n4_s2,t4_c96_n3_s1,t4_c160_n3_s2,t4_c320_n1_s1 |
| MobileNetV2 | 74.34 | (75.8) | 91.37 | 2.35 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| MobileNetV2 | 73.84 | (75.7) | 79.10 | 2.35 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s2,t6_c32_n3_s1,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| MobileNetV2 | 73.51 | (75.1) | 62.27 | 1.48 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.75_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| MobileNetV2 | 72.04 | (74.0) | 43.74 | 1.14 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.75_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c32_n3_s2,t4_c64_n4_s2,t4_c96_n3_s1,t4_c160_n3_s2,t4_c320_n1_s1 |
| MobileNetV2 | 70.35 | (72.8) | 29.92 | 0.82 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm0.5_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| MobileNetV2 | 68.15 | (70.2) | 25.67 | 2.35 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s2,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| MobileNetV2 (baseline) | 56.61 | (59.6) | 6.51 | 2.35 | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s2,t1_c16_n1_s1,t6_c24_n2_s2,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| network | top1-acc | MACs(M) | params(M) | checkpoint | ngpus | config | block_args |
|---|---|---|---|---|---|---|---|
| SEMobileNetV2 | 76.64 | 309.84 | 16.05 | TBA | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm2.0_rn8_s1,t1_c16_n1_s1,t4_c24_n2_s1,t4_c40_n2_s2,t4_c80_n3_s2,t4_c112_n3_s1,t4_c192_n4_s2,t4_c320_n1_s1 |
| SEMobileNetV2 | 75.92 | 118.90 | 7.16 | TBA | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c40_n2_s2,t6_c80_n3_s2,t6_c112_n3_s1,t6_c192_n4_s2,t6_c320_n1_s1 |
| SEMobileNetV2 | 75.10 | 94.29 | 4.61 | TBA | 4 | train-semobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
| MobileNetV2 | 74.34 | 91.37 | 2.35 | TBA | 4 | train-mobilenetv2-cifar100-b128-e200-w5-cosine-wd0.0001-lr0.1.conf | wm1.0_rn8_s1,t1_c16_n1_s1,t6_c24_n2_s1,t6_c32_n3_s2,t6_c64_n4_s2,t6_c96_n3_s1,t6_c160_n3_s2,t6_c320_n1_s1 |
Taehwan Yoo (kofmap@gmail.com)