bdd100k_seg.zip (1253.18M) 下载 File Name Size Update Time bdd100k/seg/color_labels/train/5e2209ec-cec943b4_train_color.png 10844 2018-04-06 02:08:38 bdd100k/seg/color_labels/train/4b9bdc73-821e5299_train_color.png 11974 2018-04-06 02:08:37 bdd100k/seg/color_labels/train/01c3c...
解释 加载预训练模型: 使用YOLO('yolov8n-seg.pt')加载支持分割的YOLOv8小型预训练模型。你可以根据需要选择其他大小的模型(如yolov8s-seg,yolov8m-seg,yolov8l-seg,yolov8x-seg)。 开始训练: 使用model.train()方法启动训练过程。参数包括数据集路径、训练轮数、批量大小、图像大小等。 验证模型: 使用model...
bdd100k_ins_seg_labels_trainval.zip (97.67M) 下载 File Name Size Update Time bdd100k/labels/ins_seg/bitmasks/train/c3593016-43a3f33f.png 5452 2021-04-28 03:37:39 bdd100k/labels/ins_seg/bitmasks/train/02ea9e52-b4b02f95.png 7294 2021-04-28 03:37:26 bdd100k/labels/ins_seg/bitmask...
Update sem_seg.toml (#296) Feb 3, 2023 LICENSE fix styles Sep 9, 2020 README.md Update Discussions Link (#117) May 25, 2021 pyproject.toml Move coco conversion code to Scalabel (#63) Apr 14, 2021 requirements.txt Update download link (#339) ...
main 1Branch2Tags Code Folders and files Name Last commit message Last commit date Latest commit thomasehuang Updating det README Nov 26, 2022 0935a8a·Nov 26, 2022 History 9 Commits .vscode det doc drivable ins_seg mot mots pan_seg ...
但除了目标检测外,可行驶区域及车道线只是在json文件中,并没有直接生成label图片。 本文目的是为了实现以下功能。 例如,原图 生成当前车辆的可行驶区域: 当然,原数据已经有彩色的更大行驶区域,自己感受两者的区别。 生成车道线: 但是很无奈,车道线是没法形成分割图片,原始文件格式如下,每个文件下分别有train和val两...
-bdd100k-labels-pan_seg-bitmasks-train-val-colormaps-train-val-polygons-pan_seg_train.json-pan_seg_val.json MOT 2020 Labels Multi-object bounding box tracking training and validation labels released in 2020. This is a subset of the 100K videos, but the videos are resampled to 5Hz fro...
cfg.data.train.pipeline[9]['type'] = 'SegRescale' cfg.data.train.pipeline[10]['type'] = 'PolyRandomCrop' cfg.data.train.pipeline[11]['type'] = 'CustomCopyPaste' cfg.data.train.pipeline[12]['type'] = 'LoadAnnotations' cfg.data.train.pipeline[13]['type'] = 'Resize' cfg.data.tr...
python3 -m bdd100k.label.to_color -m sem_seg|ins_seg|seg_track \ -i ${in_path} -o ${out_path} [--nproc ${process_num}] process_num: the number of processes used for the conversion. Default as 4. to_coco to_cococonverts BDD100K JSONs/masks into COCO format. For detectio...
BDD100K val VLTSeg Multiple Object Tracking BDD100K val ByteTrack Traffic Object Detection BDD100K val Tencent-MultiADNet Lane Detection BDD100K val YOLOPv2 Multi-Object Tracking and Segmentation BDD100K val UNINEXT-H Show all 16 benchmarks ...