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A PyTorch implementation for MobileNet on CIFAR100

This repository is for training MobileNets on CIFAR100 using pytorch and Docker-based DL development environment

How to Use

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

Tracking training progress with TensorBoard

To run tensorboard service to 6006 port

./docker/compose --tensorboard

Experimental Results

  • 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

Pretrained Models

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

Contact

Taehwan Yoo (kofmap@gmail.com)