Randl MobileNetV3 Pytorch Save
Implementation of MobileNetV3 in pytorch
Project README
MobileNetV3 in PyTorch
An implementation of MobileNetV3 in PyTorch. MobileNetV3 is an efficient
convolutional neural network architecture for mobile devices. For more information check the paper:
Searching for MobileNetV3
Usage
Clone the repo:
git clone https://github.com/Randl/MobileNetV3-pytorch
pip install -r requirements.txt
Use the model defined in MobileNetV3.py to run ImageNet example:
python3 -m torch.distributed.launch --nproc_per_node=8 imagenet.py --dataroot "/path/to/imagenet/" --sched clr -b 128 --seed 42 --world-size 8 --sync-bn```
To continue training from checkpoint
python imagenet.py --dataroot "/path/to/imagenet/" --resume "/path/to/checkpoint/folder"
Results
WIP
| Classification Checkpoint | MACs (M) | Parameters (M) | Top-1 Accuracy | Top-5 Accuracy | Claimed top-1 | Claimed top-5 | Inference time |
|---|---|---|---|---|---|---|---|
| MobileNetV3 Large x1.0 224 | 219.80 | 5.481 | 73.53 | 91.14 | 75.2 | - | ~258ms |
| mobilenet_v2_1.0_224 | 300 | 3.47 | 72.10 | 90.48 | 71.8 | 91.0 | ~461ms |
Inference time is for single 1080 ti per batch of 128.
You can test it with
python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/mobilenetv3large-v1/model_best0.pth.tar" -e
Other implementations
- https://github.com/d-li14/mobilenetv3.pytorch : 73.152% top-1, with more FLOPs
- https://github.com/xiaolai-sqlai/mobilenetv3 : 75.45% top-1, even more FLOPs
- https://github.com/rwightman/gen-efficientnet-pytorch : 75.634% top-1, seems to be right FLOPs
Code used
Open Source Agenda is not affiliated with "Randl MobileNetV3 Pytorch" Project. README Source: Randl/MobileNetV3-pytorch
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