1. Environment
Ubuntu 20.04
python 3.8.8
Graphics card driver 470.74
CUDA 11.5
View Ubuntu version number
cat /proc/version
View Python version number
python --version
View graphics driver version
nvidia-smi
View CUDA version
nvcc --version
2. Code model download
2.1 download model
git clone https://github.com/ultralytics/yolov5
2.2 download the pre training model
It is recommended to create a new weights in the model folder to place the downloaded pre training model weights
3. Data set production
3.1 download Yolomark
git clone https://github.com/AlexeyAB/Yolo_mark
3.2 place the pictures in Yolomark/x64/Release/data/img folder and delete the pre loaded pictures.
3.3 cmake, then run yolo_mark
cd Yolo_mark cmake . make chomd +x ./linux_mark.sh ./linux_mark.sh
3.4 modify Yolo before this_ Mark / x64 / release / data / obj.data to specify the number of dimension types
classes= 1
Modify Yolo_mark/x64/Release/data/obj.names to specify the label type name
Finger
3.5 the main interface is as follows. Box selection is used for marking, object ID selection is used to switch the standard type, c clear the current image mark, and the spacebar is used to save and flip the next image.
3.6 after labeling, the txt of the label is automatically generated, for example:
0 0.612500 0.174306 0.071875 0.184722 0 0.683984 0.120833 0.071094 0.183333 0 0.756641 0.097917 0.077344 0.168056 0 0.833594 0.134028 0.065625 0.145833 0 0.880859 0.311111 0.064844 0.155556
4. Training settings
4.1 file path setting
The yolov5 root directory is shown in the following picture. It is recommended to place each file as follows
data - put the. yaml file used for dataset and training
models -- release yolo5 model
runs - training results
Weights -- pre training model weights
Root directory - put train.py (training execution entry)
4.2 dataset settings
Create a new folder FingerDatasets under yolov5/data, where you can create two files, images and labels, and put the marked jpg pictures and txt labels.
4.3 training set and test set allocation
The labeled pictures need to be divided into three purposes: train/test/val,
Finally, three txt files are generated
txt contents are as follows
data/FingerDatasets/images/71.jpg data/FingerDatasets/images/72.jpg data/FingerDatasets/images/73.jpg data/FingerDatasets/images/74.jpg data/FingerDatasets/images/75.jpg data/FingerDatasets/images/76.jpg data/FingerDatasets/images/77.jpg data/FingerDatasets/images/78.jpg data/FingerDatasets/images/79.jpg data/FingerDatasets/images/80.jpg data/FingerDatasets/images/81.jpg
4.4 preparation of training configuration file
Create a new FingerDetect.yaml file under yolov5/data. Used to direct the application to connect to the dataset
train: data/FingerDatasets/train.txt val: data/FingerDatasets/val.txt test: data/FingerDatasets/test.txt nc: 1 names: ['FInger']
4.5 modify model
We use the v5x model, open yolov5/models/yolov5x/yaml, modify the recognition type to 1, and adjust the network depth and width appropriately. The anchor, backbone and head can be adjusted after being familiar with the network structure.
nc: 1 # number of classes depth_multiple: 1.5 # model depth multiple width_multiple: 1.33 # layer channel multiple
4.6 modification of training documents
In def parse_ In opt function, modify the training parameters according to the actual situation:
weight
parser.add_argument('--weights', type=str, default=ROOT / 'weights/yolov5s.pt', help='initial weights path')
Model
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
data set
parser.add_argument('--data', type=str, default=ROOT / 'data/FingerDetect.yaml', help='dataset.yaml path')
EPoch quantity
parser.add_argument('--epochs', type=int, default=500)
Batch size
parser.add_argument('--batch-size', type=int, default=4, help='total batch size for all GPUs, -1 for autobatch')
4.7 start training
cd yolov5 python3 train.py
5. Description of training results
The training results are placed in the latest exp folder under yolov5/runs folder by default.
Weight model - under the weights folder generated in exp
Training process - results.csv
Loss curve / precision curve - results.png
Val presentation set results - val_batchX_labels.jpg
