databunch=BertDataBunch(DATA_PATH,LABEL_PATH,tokenizer,train_file='train.csv',val_file='valid.csv',test_data='test.csv',label_file="labels.csv",text_col="comment_text",label_col=label_cols,bs=args['train_batch_size'],maxlen=args['max_seq_length'],multi_gpu=multi_gpu,multi_label=Tr...
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 data but relative image path in JSON filelabelme apc2016_obj3.jpg \ --labels highland_6539_self_stick_notes,me...
Open Dir(图片目录)>> Change Save Dir(标注文件目录:最好与图片目录相同) >>Create \nRectBox(创建标注)>>选择类名>>Save(保存)>>Next Image(下一张) 为了加快标注,你可以进入 labelImg-master\data下,用Notepad++打开predefined_classes.txt文件,修改为刚才命好这几类名字。配合快捷键,一个小时大约标注100-...
json_data_initial = copy.deepcopy(json_data)#深拷贝json文件,以防误修改info = json_data_initial["shapes"]#找到json文件的所有标注框iflen(info) ==len(basic_point):#假如使用的模板和标签数量一致forreference_point,info_pointinzip(basic_point,info):#返回一个基础点,一个标注框的所有点point_set ...
label_name_to_value[label_name]=label_value#生成标签图像lbl, _ = utils.shapes_to_label(img.shape, data["shapes"], label_name_to_value)#将数值映射到标签名称label_names = [None] * (max(label_name_to_value.values()) + 1)forname, valueinlabel_name_to_value.items(): ...
/Users/liushuaitao/opt/anaconda3/bin/activate && conda activate /Users/liushuaitao/opt/anaconda3; (base) liushuaitao@bogon ~ % conda create --name=labelme python=3.8 Collecting package metadata (current_repodata.json): done Solving environment: done ## Package Plan ## environment location: ...
# FILTER THE DATA train_label = train_label[train_label['value'] == 1] # GET THE IMAGE ID NUMBER train_label['id'] = [int(i[1]) for i in train_label['image_id'].str.split('_')] # RESET THE INDEX train_label = train_label.sort_values('id').reset_index() ...
# Install all package dependenciespip install -e .# Run database migrationspython label_studio/manage.py migrate# Start the server in development mode at http://localhost:8080python label_studio/manage.py runserver Deploy in a cloud instance ...
# 然后在终端中执行以下脚本,对 doccano 格式的数据文件进行处理,执行后会在 /home/data 目录下生成训练/验证/测试集文件。 !python finetune.py \ --train_path "./data/train.txt" \ --dev_path "./data/dev.txt" \ --save_dir "./checkpoint" \ --learning_rate 1e-5 \ --batch_size 32 \...
#Install all package dependenciespip install -e.#Run database migrationspython label_studio/manage.py migrate python label_studio/manage.py collectstatic#Start the server in development mode at http://localhost:8080python label_studio/manage.py runserver ...