yolov3_OpenImage图像训练教程

Training YOLOv3 Object Detector - Snowman 1. Install awscli `sudo pip3 install awscli` 2. Get the relevant OpenImages files needed to locate images of our interest `wget https://storage.googleapis.com/openimages/2018_04/class-descriptions-boxable.csv` `wget https://storage.googleapis.com/openimages/2018_04/train/train-annotations-bbox.csv` 3. Download the images from OpenImagesV4 `python3 getDataFromOpenImages_snowman.py` 4. Create the train-test split `python3 splitTrainAndTest.py /data-ssd/sunita/snowman/JPEGImages` Give the correct path to the data JPEGImages folder. The 'labels' folder should be in the same directory as the JPEGImages folder. 5. Install Darknet and compile it. ``` cd ~ git clone https://github.com/pjreddie/darknet cd darknet make ``` 6. Get the pretrained model `wget https://pjreddie.com/media/files/darknet53.conv.74 -O ~/darknet/darknet53.conv.74` 7. Fill in correct paths in the darknet.data file 8. Start the training as below, by giving the correct paths to all the files being used as arguments `cd ~/darknet` `./darknet detector train /path/to/snowman/darknet.data /path/to/snowman/darknet-yolov3.cfg ./darknet53.conv.74 > /path/to/snowman/train.log` 9. Give the correct path to the modelConfiguration and modelWeights files in object_detection_yolo.py and test any image or video for snowman detection, e.g. `python3 object_detection_yolo.py --image=snowmanImage.jpg`