- Labels. Image labeling we use a tool called labelimg: https://github.com/tzutalin/labelImg and create a new .yaml:
train: ../yolo_A/images/
val: ../yolo_A/images/
nc: 1
names: ['ROI']
- Divided into two folders and one file: images, labels, A.yaml and compressed into one zip file yolo_A.zip.
Enter colab and enter the yolov5 environment：
!git clone https://github.com/ultralytics/yolov5 # clone
%cd yolov5
%pip install -qr requirements.txt # install
import torch
import utils
display = utils.notebook_init() # checks
- Uncompress yolo_A.zip
!pip install pyunpack
!pip install patool
from pyunpack import Archive
Archive('/content/yolo_A.zip').extractall('/content/yolo_A')
- Train
!python train.py --img 640 --batch 8 --epochs 100 --data ../yolo_A/A.yaml --weights yolov5n.pt --nosave --cache
- Test
!python detect.py --weights /content/yolov5/runs/train/exp2/weights/last.pt --img 640 --conf 0.25 --source /content/yolo_A/images
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent