This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is ...
In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed ...
制作coco数据集,并在mmdetection上实验 一、dataset2coco 首先将标注好的json和img放在同一个文件夹中,取名为images。 1. format.py 统一修改json中的img_path。将路径修改为统一格式。×××.jpg format.py 代码如下: import os import re dir_path = '/home/chenghiuyi/03 DLA-CHD/DLA-CHD_TRAIN_NO_CHEC...
format(dataDir, dataset) # 使用COCO API用来初始化注释数据 coco = COCO(annFile) # 获取COCO数据集中的所有类别 classes = id2name(coco) # print(classes) # [1, 2, 3, 4, 6, 8] classes_ids = coco.getCatIds(catNms=classes_names) # print(classes_ids) for cls in classes_names: # ...
info:{'description':'COCO 2014 Dataset','url':'http://cocodataset.org','version':'1.0','year':2014,'contributor':'COCO Consortium','date_created':'2017/09/01'}categories:[{'supercategory':'person','id':1,'name':'person'}...Lengthofimages:40504{'license':3,'file_name':'COCO_va...
# 生成val.json !python tools/x2coco.py \ --dataset_type voc \ --voc_anno_dir /home/aistudio/work/voc_data/Annotations \ --voc_anno_list /home/aistudio/work/voc_data/val.txt \ --voc_label_list /home/aistudio/work/voc_data/label_list.txt \ --voc_out_name val.json Start conve...
5.统计分类信息,写入txt文件中 """ import json import os from pycocotools.coco import COCO import cv2 import random # 使用环境变量或配置文件来设置路径 train_dir = os.getenv('TRAIN_DIR', "./dataset/coco/train") val_dir = os.getenv('VAL_DIR', "./dataset/coco/val") test_dir = os....
COCO(Common Objects in COtext)数据集,是一个大规模的,适用于目标检测,图像分割,Image Captioning任务的数据集,其标注格式是最常用的几种格式之一。目前使用较多的是COCO2017数据集。其官网为COCO - Common Objects in Context (cocodataset.org)。 COCO数据集主要包含图像(jpg或者png等等)和标注文件(json),其数据...
# 现在的我只需要加载图像和对应的label即可 不需要加载text中的内容 def load_satetile_image(batch_size=128, dataset='train'): img_list = [] label_list = [] dir_counter = 0 if dataset == 'train': path = '../Dataset/baidu/train_image/train' ...
image_path x_min, y_min, x_max, y_max, class_id x_min, y_min ,…, class_id make sure that x_max < width and y_max < height 针对tensorflow-yolov3,它的class_id 是从0开始到类别数-1. 而我自己的数据集的category_id部分是1,2,3,4,8,9,10 ...