A trick for using YOLOv5
source link: https://donghao.org/2022/09/30/a-trick-for-using-yolov5/
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A trick for using YOLOv5
To detect birds and squirrels, we created a dataset to train the YOLOv5 model. After a week’s training with:
python3 -u train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 28 --workers 1
python3 -u train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 28 --workers 1
The model could recognize birds and squirrels properly except only for this image:
Why does the model recognize the right-side significant squirrel as a bird? Even though I tried a bigger model, the result was the same…
Only after researching the parameters of the function model() of YOLOv5, I found out we can use a different image size: 960 for detecting.
import inspect
import torch
import cv2
model = torch.hub.load('.', 'custom', path='last.pt', source='local')
#model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
model.eval()
image = cv2.imread(img)
results = model(img, size=960)
results.show()
import inspect
import torch
import cv2
model = torch.hub.load('.', 'custom', path='last.pt', source='local')#model = torch.hub.load('ultralytics/yolov5', 'yolov5s')model.eval()
image = cv2.imread(img)
results = model(img, size=960)
results.show()
The result is below for model(img, size=960)
Hmm, seems the single-stage YOLOv5 model is nearsighted, just like me…
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September 30, 2022 - 4:32
RobinDong
machine learning
YOLO, YOLOv5
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