File "/home/lunet/fylr3/.conda/envs/openmmlab/lib/python3.8/site-packages/pycocotools/coco.py", line 81, in __init__ with open(annotation_file, 'r') as f: FileNotFoundError: [Errno 2] No such file or directory:
├──coco2017:数据集根目录├──train2017:所有训练图像文件夹(118287张)├──val2017:所有验证图像文件夹(5000张)└──annotations:对应标注文件夹├──instances_train2017.json:对应目标检测、分割任务的训练集标注文件├──instances_val2017.json:对应目标检测、分割任务的验证集标注文件├──captions_train...
annotations_file=os.path.join('/home/lmin/data/coco/annotations','instances_val2017.json') batch_size=16 pipe=Pipeline(batch_size=batch_size,num_threads=4,device_id=0) withpipe: jpegs,bboxes,labels,polygons,vertices=fn.coco_reader( file_root=file_root, annotations_file=annotations_file, ...
│ │ │ ├──instances_train2017.2@1-unlabeled.json │ │ ├── test2017 │ │ ├── train2017 │ │ ├── unlabeled2017 │ │ ├── val2017 The data set used at the end: mmdetection ├── data │ ├── coco │ │ ├── annotations │ │ │ ├──image_info_unlabeled...
"txt_path: 将json文件处理后txt文件存放的文件夹名" # 生成存放json文件的路径 if not os.path.exists(txt_path): os.mkdir(txt_path) # 读取json文件 with open(json_path, 'r') as f: dict = json.load(f) # 得到images和annotations信息 ...
"""is_coco_meta=True# Mandatory.# You must declare this variable, otherwise DDS will ignore it.dataset_name="instances_val2017"# Mandatory.# The name of the dataset.ground_truth="annotations/instances_val2017.json"# Mandatory.# The ground truth file path, relative to the directory of this...
The annotations are saved as aJSONfile. labelme# just open gui# tutorial (single image example)cdexamples/tutorial labelme apc2016_obj3.jpg# specify image filelabelme apc2016_obj3.jpg -O apc2016_obj3.json# close window after the savelabelme apc2016_obj3.jpg --nodata# not include image...
/// 将json转换为DataTable /// /// 得到的json /// <returns></returns> private DataTable JsonToDataTable(string strJson) { //转换json格式 strJson = strJson.Replace(",\"", "*\"").Replace("\":", "\"#").ToString(); //取出表名 var rg = new Regex(@"(?<=...
The kitti_seq_to_map.json file contains a sequence to frame ID mapping for the frames in the images directory. This is an optional file and is useful if the data needs to be split into N folds sequence wise. In case the data is to be split into a random 80:20 train:val split, ...
${EVAP_ROOT} -- datasets -- ytvis_2019 -- train -- val -- annotations -- instances_train_sub.json -- instances_val_sub.json -- ytvis19_cocofmt.json -- ytvis_2021 -- train -- val -- annotations -- instances_train_sub.json -- instances_val_sub.json -- ytvis21_cocofmt.json...