Run pip uninstall ultralytics to clean the ultralytics library installed in the environment
Then run python setup.py develop
My experimental environment:
torch: 1.13.1
torchvision: 0.14.1
Some additional package installation commands:
pip install timm thop efficientnet_pytorch einops -i https://pypi.tuna.tsinghua.edu.cn/simple
The following are mainly used dyhead must be installed packages, if the installation is not successful dyhead can not be used normally!
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"
Modify 'yaml' to choose whether to add Slim-neck, C2f-faster, EMA attention module, and dyhead. Modify 'data' to select the data set to use. The following are training parameters that can be modified, with an explanation of each parameter at the end of each line.
parser.add_argument('--yaml', type=str, default='ultralytics/models/v8/yolov8n.yaml', help='model.yaml path')
parser.add_argument('--weight', type=str, default='', help='pretrained model path')
parser.add_argument('--cfg', type=str, default='hyp.yaml', help='hyperparameters path')
parser.add_argument('--data', type=str, default='ultralytics/datasets/coco128.yaml', help='data yaml path')
parser.add_argument('--epochs', type=int, default=100, help='number of epochs to train for')
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
parser.add_argument('--unamp', action='store_true', help='Unuse Automatic Mixed Precision (AMP) training')
parser.add_argument('--batch', type=int, default=16, help='number of images per batch (-1 for AutoBatch)')
parser.add_argument('--imgsz', type=int, default=640, help='size of input images as integer')
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
parser.add_argument('--device', type=str, default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--workers', type=int, default=4, help='max dataloader workers (per RANK in DDP mode)')
parser.add_argument('--project', type=str, default=ROOT / 'runs/train', help='save to project/name')
parser.add_argument('--name', type=str, default='exp', help='save to project/name')
parser.add_argument('--resume', type=str, default='', help='resume training from last checkpoint')
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'Adamax', 'NAdam', 'RAdam', 'AdamW', 'RMSProp', 'auto'], default='SGD', help='optimizer (auto -> ultralytics/yolo/engine/trainer.py in build_optimizer funciton.)')
parser.add_argument('--close_mosaic', type=int, default=0, help='(int) disable mosaic augmentation for final epochs')
parser.add_argument('--info', action="store_true", help='model info verbose')
parser.add_argument('--save', type=str2bool, default='True', help='save train checkpoints and predict results')
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--seed', type=int, default=0, help='Global training seed')
parser.add_argument('--deterministic', action="store_true", default=True, help='whether to enable deterministic mode')
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
parser.add_argument('--fraction', type=float, default=1.0, help='dataset fraction to train on (default is 1.0, all images in train set)')
parser.add_argument('--profile', action='store_true', help='profile ONNX and TensorRT speeds during training for loggers')
# Segmentation
parser.add_argument('--overlap_mask', type=str2bool, default='True', help='masks should overlap during training (segment train only)')
parser.add_argument('--mask_ratio', type=int, default=4, help='mask downsample ratio (segment train only)')
# Classification
parser.add_argument('--dropout', type=float, default=0.0, help='use dropout regularization (classify train only)')
The following validation parameters can be modified, with an explanation of each parameter at the end of each line.
parser.add_argument('--weight', type=str, default='yolov8n.pt', help='training model path')
parser.add_argument('--data', type=str, default='ultralytics/datasets/coco128.yaml', help='data yaml path')
parser.add_argument('--imgsz', type=int, default=640, help='size of input images as integer')
parser.add_argument('--batch', type=int, default=16, help='number of images per batch (-1 for AutoBatch)')
parser.add_argument('--split', type=str, default='test', choices=['train', 'val', 'test'], help='dataset split to use for validation, i.e. val, test or train')
parser.add_argument('--project', type=str, default='runs/val', help='project name')
parser.add_argument('--name', type=str, default='exp', help='experiment name (project/name)')
parser.add_argument('--save_txt', action="store_true", help='save results as .txt file')
parser.add_argument('--save_json', action="store_true", help='save results to JSON file')
parser.add_argument('--save_hybrid', action="store_true", help='save hybrid version of labels (labels + additional predictions)')
parser.add_argument('--conf', type=float, default=0.001, help='object confidence threshold for detection (0.001 in val)')
parser.add_argument('--iou', type=float, default=0.65, help='intersection over union (IoU) threshold for NMS')
parser.add_argument('--max_det', type=int, default=300, help='maximum number of detections per image')
parser.add_argument('--half', action="store_true", help='use half precision (FP16)')
parser.add_argument('--dnn', action="store_true", help='use OpenCV DNN for ONNX inference')
parser.add_argument('--plots', action="store_true", default=True, help='ave plots during train/val')
The following detection parameters can be modified, with an explanation of each parameter at the end of each line.
parser.add_argument('--weight', type=str, default='yolov8n.pt', help='training model path')
parser.add_argument('--source', type=str, default='ultralytics/assets', help='source directory for images or videos')
parser.add_argument('--conf', type=float, default=0.25, help='object confidence threshold for detection')
parser.add_argument('--iou', type=float, default=0.7, help='intersection over union (IoU) threshold for NMS')
parser.add_argument('--mode', type=str, default='predict', choices=['predict', 'track'], help='predict mode or track mode')
parser.add_argument('--project', type=str, default='runs/detect', help='project name')
parser.add_argument('--name', type=str, default='exp', help='experiment name (project/name)')
parser.add_argument('--show', action="store_true", help='show results if possible')
parser.add_argument('--save_txt', action="store_true", help='save results as .txt file')
parser.add_argument('--save_conf', action="store_true", help='save results with confidence scores')
parser.add_argument('--show_labels', action="store_true", default=True, help='show object labels in plots')
parser.add_argument('--show_conf', action="store_true", default=True, help='show object confidence scores in plots')
parser.add_argument('--vid_stride', type=int, default=1, help='video frame-rate stride')
parser.add_argument('--line_width', type=int, default=3, help='line width of the bounding boxes')
parser.add_argument('--visualize', action="store_true", help='visualize model features')
parser.add_argument('--augment', action="store_true", help='apply image augmentation to prediction sources')
parser.add_argument('--agnostic_nms', action="store_true", help='class-agnostic NMS')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
parser.add_argument('--retina_masks', action="store_true", help='use high-resolution segmentation masks')
parser.add_argument('--boxes', action="store_true", default=True, help='Show boxes in segmentation predictions')
parser.add_argument('--save', action="store_true", default=True, help='save result')
parser.add_argument('--tracker', type=str, default='bytetrack.yaml', choices=['botsort.yaml', 'bytetrack.yaml'], help='tracker type, [botsort.yaml, bytetrack.yaml]')
The model configuration files are in ultralytics/models/v8. yolov8 comes in five sizes
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
How do you specify which size model to use? Suppose I want to choose the size of m model, the yaml parameters in the train.py should be specified as ultralytics/models/v8/yolov8m.yaml.
You only need to specify the info parameter in the training command.
python train.py --yaml ultralytics/models/v8/yolov8n-fasternet.yaml --info