项目作者： Minsung-kk
项目描述 ：
YOLOv5 Object Detection with Labeled Pine Cone Images
高级语言： Jupyter Notebook
项目地址: git://github.com/Minsung-kk/YOLOv5-pinecone.git
YOLOv5-pinecone
YOLOv5 Object Detection with Labeled Pine Cone Images
- original video link: https://www.youtube.com/watch?v=T0DO1C8uYP8 (in Korean)
- brief instruction using Google Colaborator
- so we can use free provided GPU (Runtime - Change runtime type - GPU)
- change directory to content
- clone YOLOv5
- install requirements
- %cd /content/yolov5/
- !pip install -r requirements.txt
- you can download weights ‘best.pt’ and ‘last.pt’, and jump to the detect section to use them from here
- trained with more images and added ‘best_ext.pt’ and ‘last_ext.pt’
- make a new folder and name it as ‘dataset’ and upload your dataset
- uploaded files will be deleted after this session
- you can upload your dataset to Google Drive, so you don’t need to upload everytime
- split dataset into train and validation
- only if the dataset hasn’t been splitted. otherwise just set train and valid folders in data.yaml file
- %cd /
- from glob import glob
- img_list = glob(‘/content/dataset/(location of images)/*.jpg’)
- from sklearn.model_selection import train_test_split
- train, val = train_test_split(img_list, test_size=0.2, random_state=2000)
- save locations of train and val
- with open(‘/content/dataset/train.txt’, ‘w’) as f:
- f.write(‘\n’.join(train) + ‘n’)
- with open(‘/content/dataset/val.txt’, ‘w’) as f:
- f.write(‘\n’.join(val) + ‘n’)
- change data.yaml to indicate above train and val locations (can simply open the file and edit also)
- import yaml
- with open(‘/content/dataset/data.yaml’, ‘r’) as f:
- data[‘train’] = ‘/content/dataset/train.txt’ (or a train directory)
- data[‘val’] = ‘/content/dataset/val.txt’ (or a val directory)
- with open(‘/content/dataset/data.yaml’, ‘w’) as f:
- modify nc as 1 since we only want to detect pincones
- training
- %cd /content/yolov5/
- !python train.py —img 640 —batch 16 —epochs 50 —data /content/dataset/data.yaml —cfg ./models/yolov5s.yaml —weights yolov5s.pt —name pinecones_yolov5s_results
- detecting
- %cd /content/yolov5/
- !python detect.py —weights /content/yolov5/runs/train/pinecones_yolov5s_results/weights/best.pt —img 640 —conf 0.5 —source (location of videos or photos)
