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: # ...
if json_label["label"] not in CLASS_REAL_NAMES: print('error', json_label["label"], file) class_ids.append(json_label["label"]) class_ids = np.unique(class_ids) print('一共有{}种class'.format(len(class_ids))) print('分别是') index = 1 for id in class_ids: print('"{}"...
I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))#通过id,得到注释的信息annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)# print(annIds)anns = coco.loadAnns(annIds)# print(anns)# coco.showAnns(anns)objs = []foranninanns: class_name=classes[an...
一check原数据集的class信息跟bbox信息 二json解析 三 生成图片对应的绝对路径的train.txt与 val.txt 四 更改darknet目录下的.data与.names文件 五 修改yolov3.cfg文件 六train 一check原数据集的class信息跟bbox信息 see一下coco json里面的形式,因为只做ob,所以只需要提取bbox就可以了,下面要看一下bbox对应...
class CatDog(CocoDataset): CLASSES = ('dog', 'cat') 在mmdet/datasets/__init__.py: from .cat_dog import CatDog 1.2.2 修改 faster_rcnn 模型配置 下载resnet50的预训练模型,放入$TORCH_HOME, export TORCH_HOME=/data1/Projects/pretrained_models ...
Ultralytics COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. ...
class_names, split=train_split, format=img_format, transforms=preview_transform) output_file = f'instances_{train_split[:-4]}.json' for i, sample in enumerate(voc_dataset): utils.progress_bar(i, len(voc_dataset), 'Drawing...') image = sample['image'] bboxes = sample['bboxes']....
classes_names = ['person'] #coco有80类，这⾥写要提取类的名字，以person为例 #Store annotations and train2014/val2014/... in this folder dataDir= '/media/huanglong/Newsmy/COCO/' #原coco数据集 headstr = """\ <annotation> <folder>VOC</folder> <filename>%s</filename> <database>My...
下面就是一个简单的SimpleCoCoDataset类 classSimpleCoCoDataset(Dataset):def__init__(self, rootdir, set_name='val2017', transform=None): self.rootdir, self.set_name = rootdir, set_name self.transform = transform self.coco = COCO(os.path.join(self.rootdir,'annotations','instances_'+ self.set...
split to make results comparable. The dataset includes 80 thing classes, 91 stuff classes and 1 class 'unlabeled'. This was initially presented as 91 thing classes, but is now changed to 80 thing classes, as 11 classesdo not have any segmentation annotationsin COCO. This dataset is a subset...