Some parameter settings and feature graph visualization of yolov5-6.0

1、detect.py

def parse_opt():
    parser = argparse.ArgumentParser()
    #  Model 
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
    # parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5l.pt', help='model path(s)')
    #  need detect The file of ： picture 、 video 
    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
    # train dataset The label of 
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    #  Believe is a class The threshold of 
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    # IoU
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    #  Just look at some classes
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    #  Model enhancement , If specified, it is set to True
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    #  Remove unnecessary parts of the network model 
    parser.add_argument('--update', action='store_true', help='update all models')
    #  Saved file name 
    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    #  Whether to put it in a separate folder  True:do not increment
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(FILE.stem, opt)
    return opt

2、train.py

def parse_opt(known=False):
    parser = argparse.ArgumentParser()
    #  Trained models , Set to blank to train from the beginning 
    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
    #  Parameter configuration of the model ,  Such as models/yolov5s.yaml
    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
    # dataset path（ Label file ）
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
    #  Hyperparameters , Fine tune the model parameters 
    parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
    #  Number of training rounds 
    parser.add_argument('--epochs', type=int, default=300)
    # --batch-size
    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
    # img-size
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
    #  Non matrix size image processing 
    parser.add_argument('--rect', action='store_true', help='rectangular training')
    #  Whether to continue training from the latest training model , default="path"（ Complete configuration is required ）
    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
    #  Save only the last training model ？
    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
    #  Only test the last training model ？
    parser.add_argument('--noval', action='store_true', help='only validate final epoch')
    #  Anchor point 
    parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
    # “ evolution ” Hyperparameters 
    parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
    # ---- Never mind ----
    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
    #  Whether to cache the picture , Easy to train 
    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
    #  Add some weight to those who failed in the previous round of tests , The effect is hard to say 
    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    #  Picture size transformation 
    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
    #  Whether the training data set is single category or multi category 
    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
    #  Optimizer settings 
    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
    #  many GPU
    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
    #  Number of processes 
    # parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
    parser.add_argument('--workers', type=int, default=0, help='max dataloader workers (per RANK in DDP mode)')
    #  Save the path 
    parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
    parser.add_argument('--name', default='exp', help='save to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    #  Data processing 
    parser.add_argument('--quad', action='store_true', help='quad dataloader')
    # learning rate
    parser.add_argument('--linear-lr', action='store_true', help='linear LR')
    #  Label smoothing 
    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
    parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
    parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
    #  preservation W$B journal 
    parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
    # Weights & Biases arguments
    parser.add_argument('--entity', default=None, help='W&B: Entity')
    parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
    parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
    parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
    opt = parser.parse_known_args()[0] if known else parser.parse_args()
    return opt

3、 Environment configuration 】YOLOV5 View the characteristic diagram

4、yolov5 Visual feature map and test results

5、YOLO-V5 GRADCAM

6、YOLOv5 Target detection Grad-CAM Thermodynamic diagram visualization