erasing: 0.4- 在分类训练中随机擦除的概率,范围为 0-0.9,用于模拟部分遮挡。 crop_fraction: 1.0- 图像裁剪比例,用于分类,范围为 0.1-1,1.0 表示不裁剪。 在检测交通标志的场景中,fliplr左右翻转增强可能是不合适的,因为某些标志翻转后意义就完全变了,这时就需要结合实际业务场景,避免因不合适的数据增强而引起模...
crop_fractionfloat1.00.1 - 1.0将分类图像裁剪为其大小的一小部分,以突出中心特征并适应对象比例,减少背景干扰。 这些设置可根据数据集和手头任务的具体要求进行调整。试验不同的值有助于找到最佳的增强策略,从而获得最佳的模型性能。 信息 有关增强训练行动的更多信息,请参阅参考资料部分。
1,2,3 或 device=cpuworkers:8# (int) 数据加载的工作线程数(如果是 DDP,则每个 RANK)project:# (str, optional可选) 项目名称name:# (str, optional) 实验名称,结果保存到 '项目/名称' 目录exist_ok:False# (bool) 是否覆盖现有实验pretrained:True# (bool | str) 是否使用预训练模型...
min_dim=2) # check image size self.transforms = ( getattr( self.model.model, "transforms", classify_transforms(self.imgsz[0], crop_fraction=self.args.crop_fraction), ) if self.args.task == "classify" else None ) self.dataset = load_inference_source( source=source, batch=self.args.ba...
args.crop_fraction), ) if self.args.task == "classify" else None ) self.dataset = load_inference_source( source=source, batch=self.args.batch, vid_stride=self.args.vid_stride, buffer=self.args.stream_buffer, ) self.source_type = self.dataset.source_type if not getattr(self, "stream"...
txt file save_conf: False # save results with confidence scores save_crop: False # save cropped images with results hide_labels: False # hide labels hide_conf: False # hide confidence scores vid_stride: 1 # video frame-rate stride line_thickness: 3 # bounding box thickness (pixels) ...
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. | 17 changes: 9 additions & 8 deletions 17 docs/en/models/index.md Original file...
] -= crop_box[:2] boxes[:, 2:] -= crop_box[:2] mask = np.logical_and(mask, (boxes[:, :2] < boxes[:, 2:]).all(axis=1)) boxes = boxes * np.expand_dims(mask.astype('float32'), axis=1) labels = labels * mask.astype('float32'...
# 每张GPU reader进程个数 worker_num: 2 # 训练数据 TrainReader: inputs_def: num_max_boxes: 50 # 训练数据transforms sample_transforms: - Decode: {} - Mixup: {alpha: 1.5, beta: 1.5} - RandomDistort: {} - RandomExpand: {fill_value: [123.675, 116.28, 103.53]} - RandomCrop: {} - ...
shape[1]>bg_w: return None,None mask = (crop_rotate != np.array(list(borderValue))) mask = (mask[:, :, 0] | mask[:, :, 1] | mask[:, :, 2]) mask_inv=(~mask) roi = background_image_cp[random_y:random_y + crop_rotate.shape[0], random_x:random_x + crop_...