Check if the dataset format is compatible and upload it as W&B artifact arguments: opt (namespace)-- Commandline argumen

class-aware result, return p[i], r[i], ap50[i], ap[i]

Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form [x,y,s,r] where x,y is the center of the box and s is the scale/area an

Takes a bounding box in the centre form [x,y,s,r] and returns it in the form [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bot

Create and return W&B artifact containing W&B Table of the dataset. arguments: dataset -- instance of LoadImagesAndLabel

download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX arguments: path -- path of the data

Transpose a PIL image accordingly if it has an EXIF Orientation tag. Inplace version of https://github.com/python-pillow/Pillow/blob/master

Sanitize the graph by cleaning any unconnected nodes, do a topological resort, and fold constant inputs values. When possible, run

Check if the process runs inside a docker container.

Log files (images) as debug samples in the ClearML task. arguments: files (List(PosixPath)) a list of file paths in Posi

Map the validation dataset Table like name of file -> it's id in the W&B Table. Useful for - referencing artifacts for evaluation.

Mean of results, return mp, mr, map50, map

Args: img_size (tuple): The image size. polygons (np.ndarray): [N, M], N is the number of polygons, M is the numb

Return correct prediction matrix Arguments: detections (array[N, 6]), x1, y1, x2, y2, conf, class labels (array[M, 5]), c

YOLOv5 speed/memory/FLOPs profiler Usage: input = torch.randn(16, 3, 640, 640) m1 = lambda x: x * torch.sigmoid(x) m2

img1_shape: model input shape, [h, w] img0_shape: origin pic shape, [h, w, 3] masks: [h, w, num] resize for the most time