A PyTorch implementation of MobileNetV3
This is a PyTorch implementation of MobileNetV3 in the paper Searching for MobileNetV3.
We have tried to follow every detail in the original paper. Discussions are welcomed to improve this work.
[News] We are training MobileNetV3 Small on ImageNet.
Details
See implementation in mobilenetv3.py and training code in main.py.
MobileNetV3 large
| Madds | #Params | Top1-acc | Pretrained Model | |
|---|---|---|---|---|
| Offical | 219 M | 5.4 M | 75.2% | - |
| Ours | - | 5.11 M | - | - |
MobileNetV3 small
| Madds | #Params | Top1-acc | Pretrained Model | |
|---|---|---|---|---|
| Offical | 66 M | 2.9 M | 67.4% | - |
| Ours | - | 3.11 M | - | - |
Usage
➜ python main.py --help
usage: main.py [-h] [--data-path DATA_PATH] [--device DEVICE] [-b BATCH_SIZE]
[--epochs N] [-j N] [--lr LR] [--momentum M] [--wd W]
[--lr-step-size LR_STEP_SIZE] [--lr-gamma LR_GAMMA]
[--print-freq PRINT_FREQ] [--output-dir OUTPUT_DIR]
[--resume RESUME] [--start-epoch N] [--cache-dataset]
[--sync-bn] [--test-only] [--pretrained]
[--world-size WORLD_SIZE] [--dist-url DIST_URL]
PyTorch Classification Training
optional arguments:
-h, --help show this help message and exit
--data-path DATA_PATH
dataset
--device DEVICE device
-b BATCH_SIZE, --batch-size BATCH_SIZE
--epochs N number of total epochs to run
-j N, --workers N number of data loading workers (default: 16)
--lr LR initial learning rate
--momentum M momentum
--wd W, --weight-decay W
weight decay (default: 1e-4)
--lr-step-size LR_STEP_SIZE
decrease lr every step-size epochs
--lr-gamma LR_GAMMA decrease lr by a factor of lr-gamma
--print-freq PRINT_FREQ
print frequency
--output-dir OUTPUT_DIR
path where to save model
--resume RESUME resume from checkpoint
--start-epoch N start epoch
--cache-dataset Cache the datasets for quicker initialization. It also
serializes the transforms
--sync-bn Use sync batch norm
--test-only Only test the model
--pretrained Use pre-trained models from the modelzoo
--world-size WORLD_SIZE
number of distributed processes
--dist-url DIST_URL url used to set up distributed training