single_cls: 数据集是否只用一个类别,运行val.py传入默认False 运行train.py则传入single_cls augment: 是否使用数据增强的方式进行检测,默认为False verbose: 是否打印出每个类别的mAP,运行val.py传入默认Fasle。运行train.py则传入nc < 50 and final_epoch save_txt: 是否将检测结果保存为文本文件,默认为False sa...
pt, optimizer=auto, verbose= True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save _json=False, save_...
每个目标一行,整个图片没有目标的话不需要有txt文件,或者空文件。 每行的格式为class_num x_center y_center width height 其中class_num取值为0至total_class - 1,框的四个值x_centery_centerwidthheight是相对于图片分辨率大小正则化的0-1之间的数,左上角为(0,0),右下角为(1,1) 最终的标签文件应该是...
add_argument('--single-cls', action='store_true', help='treat as single-class dataset') # 测试是否使用TTA Test Time Augment 默认False parser.add_argument('--augment', action='store_true', help='augmented inference') # 是否打印出每个类别的mAP 默认False parser.add_argument('--verbose', ...
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only...
def get_dataset_class(xml_root): classes = [] for root, dirs, files in os.walk(xml_root): if files is not None: for file in files: if file.endswith('.xml'): xml_path = os.path.join(root, file) dict_info = parse_xml(xml_path) ...
('--multi-scale', default=True, help='vary img-size +/- 50%%') # single-cls: 数据集是否多类/默认True parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') # optimizer: 优化器选择 / 提供了三种优化器 parser.add_argument('--adam...
single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact...
single-cls:train as single-class dataset,暂时没用; 解释一下result.png里都是啥: GIoU:推测为GIoU损失函数均值,越小方框越准; Objectness:推测为目标检测loss均值,越小目标检测越准; Classification:推测为分类loss均值,越小分类越准; Precision:准确率(找对的/找到的); ...
('--single-cls',action='store_true',help='train multi-class data as single-class')parser.add_argument('--adam',action='store_true',help='use torch.optim.Adam() optimizer')parser.add_argument('--sync-bn',action='store_true',help='use SyncBatchNorm, only available in DDP mode')...