pytorch

YOLOv5

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Step 1: Installing packages

## clone yolov5 and install
git clone https://gitee.com/deep-spark/deepsparkhub-GPL.git
cd deepsparkhub-GPL/cv/detection/yolov5/pytorch/
bash init.sh

Step 2: Preparing datasets

Go to visit COCO official website, then select the COCO dataset you want to download.

Take coco2017 dataset as an example, specify /path/to/coco2017 to your COCO path in later training process, the unzipped dataset path structure sholud look like:

coco2017
├── annotations
│   ├── instances_train2017.json
│   ├── instances_val2017.json
│   └── ...
├── train2017
│   ├── 000000000009.jpg
│   ├── 000000000025.jpg
│   └── ...
├── val2017
│   ├── 000000000139.jpg
│   ├── 000000000285.jpg
│   └── ...
├── train2017.txt
├── val2017.txt
└── ...

Modify the configuration file(data/coco.yaml)

vim data/coco.yaml
# path: the root of coco data
# train: the relative path of train images
# val: the relative path of valid images

Training the detector

Train the yolov5 model as follows, the train log is saved in ./runs/train/exp

On single GPU

python3 train.py --data ./data/coco.yaml --batch-size 32 --cfg ./models/yolov5s.yaml --weights ''

On single GPU (AMP)

python3 train.py --data ./data/coco.yaml --batch-size 32 --cfg ./models/yolov5s.yaml --weights '' --amp

Multiple GPUs on one machine

# eight cards
# YOLOv5s
python3 -m torch.distributed.launch --nproc_per_node 8 \
    train.py \
    --data ./data/coco.yaml \
    --batch-size 64 \
    --cfg ./models/yolov5s.yaml --weights '' \
    --device 0,1,2,3,4,5,6,7

# YOLOv5m
bash run.sh

Multiple GPUs on one machine (AMP)

# eight cards 
python3 -m torch.distributed.launch --nproc_per_node 8 \
    train.py \
    --data ./data/coco.yaml \
    --batch-size 256 \
    --cfg ./models/yolov5s.yaml --weights '' \
    --device 0,1,2,3,4,5,6,7 --amp

Test the detector

Test the yolov5 model as follows, the result is saved in ./runs/detect:

python3 detect.py --source ./data/images/bus.jpg --weights yolov5s.pt --img 640

python3 detect.py --source ./data/images/zidane.jpg --weights yolov5s.pt --img 640

Results on BI-V100

GPUs	FP16	Batch size	FPS	E2E	[email protected]
1x1	True	64	81	N/A	N/A
1x8	True	64	598	24h	0.632

Convergence criteria	Configuration (x denotes number of GPUs)	Performance	Accuracy	Power（W）	Scalability	Memory utilization（G）	Stability
mAP:0.5	SDK V2.2, bs:128, 8x, AMP	1228	0.56	140*8	0.92	27.3*8	1

Reference

• YOLOv5