目标检测-基于Pytorch实现的MobileNetV3目标检测算法-附项目源码-优质项目实战.zip

# A PyTorch implementation of MobileNetV3 I retrain the mobilenetv3 with some novel tricks and [timm](https://github.com/huggingface/pytorch-image-models). I also provide the train code, pre-training weight and training logs on this project. You should use torch.load to load the model. ``` from mobilenetv3 import MobileNetV3_Small, MobileNetV3_Large # MobileNetV3_Small net = MobileNetV3_Small() net.load_state_dict(torch.load("450_act3_mobilenetv3_small.pth", map_location='cpu')) # MobileNetV3_Large net = MobileNetV3_Large() net.load_state_dict(torch.load("450_act3_mobilenetv3_large.pth", map_location='cpu')) ``` You could reproduce the model by the code. ``` nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_small --epochs 300 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint & nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_small --epochs 450 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint & nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_large --epochs 300 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint & nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_large --epochs 450 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint & ``` Some details may be different from the original paper, welcome to discuss and help me figure it out. ### MobileNetV3 | | Madds | Parameters | Top1-acc | | ------------------- | --------- | ---------- | --------- | | Small (paper) | 66 M | 2.9 M | 67.4% | | Small (torchvision) | 62 M | 2.5 M | 67.7% | | Small (our 300 epoch) | 69 M | 3.0 M | 68.9% | | Small (our 450 epoch) | 69 M | 3.0 M | 69.2% | | Large (paper) | 219 M | 5.4 M | 75.2% | | Large (torchvision) | 235 M | 5.5 M | 74.0% | | Large (our 300 epoch) | 241 M | 5.2 M | 75.6% | | Large (our 450 epoch) | 241 M | 5.2 M | 75.9% |