In this data set, we identify 5 differents classes : (bees, wasps, european hornet, asian hornet, oriental hornet)

git clone git@github.com:ultralytics/yolov5.git
cd yolov5
pip install -r requirements.txt

If you want to retrain the yolov5 model to add more classes, train on your own dataset, or for other reason, you can make your other version like this:

cd dataset
unzip Hymenoptera.v2i.yolov5pytorch.zip
cd ../yolov5
python train.py --img 640 --batch 16 --epochs 100 --data ../dataset/Hymenoptera.v2i.yolov5pytorch/data.yaml --weights yolov5m.pt

(You can add --device cpu to use CPU. Default is GPU.)

You can see the train and validate results in yolov5/runs/train/exp.
If the model looks good, you can run the following command before use it:

mv ./yolov5/runs/train/exp/weights/best.pt ./models/yolov5m.pt

For example, you can run our model on a simple image, like that:

./run.sh yolov5m_v2 ./src/image_1.jpg

The first argument is the model to use (yolov5m_v1 or yolov5m_v2). The second argument is the path to the image to run. It's work also on video files. If you want to run the model on the webcam, you can give 0 as the second argument.
The result is save on the ./src/results/ folder.

In the models folder, you can find our 2 version of yolov5 trained.

Is trained with a dataset buildt using google image, label flow and roboflow. We have around 580 datas include all classes.
There is the confusion matrix on validations data : There is some exemple of waiting vs prredicted data :

Is trained with a dataset buildt using google image, label flow and roboflow. We have around 2k datas include all classes.
There is the confusion matrix on validations data : There is some exemple of waiting vs prredicted data :