2014版本的coco dataset包括82,783 个训练图像、40,504个验证图像以及40,775个测试图像,270k的分割出来的人以及886k的分割出来的物体。 80类物体类别: { person # 1 vehicle 交通工具 #8 {bicycle car motorcycle airplane bus train truck boat} outdoor #5 {traffic light fire hydrant stop sign parking me...
image = sample['image'] bboxes = sample['bboxes'].cpu().numpy() labels = sample['labels'].cpu().numpy() image_path = sample['path'] h, w, _ = image.shape global_image_id += 1 coco_dataset['images'].append({ 'file_name': os.path.basename(image_path), 'id': global_...
官网地址:http://cocodataset.org COCO数据集格式 COCO_2017/ ├── val2017 # 总的验证集 ├── train2017 # 总的训练集 ├── annotations # COCO标注 │ ├── instances_train2017.json # object instances(目标实例) ---目标实例的训练集标注 │ ├── instances_val2017.json # object instance...
处理COCO数据集的代码如下: importosimportcv2importtorchimportnumpyasnpimportrandomfromtorch.utils.dataimportDatasetfrompycocotools.cocoimportCOCOimporttorch.nn.functionalasFCOCO_CLASSES=["person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","st...
--dataset_spec $DATA_POSE_SPECS_DIR/coco_spec.json 要将此示例用于自定义数据集,请执行以下操作: 以类似于 COCO 数据集的格式准备数据和注释。 在data \ u pose \ u config 下创建一个数据集规范,类似于 coco \ u spec . json ,其中包括数据集路径、姿势配置、遮挡标记约定等。
val_json = os.getenv('VAL_JSON', "./dataset/coco/instances_val2017.json") def load_coco_json(json_file): try: coco = COCO(json_file) except Exception as e: print(f"Error loading JSON file: {json_file}. Error: {str(e)}") ...
COCO dataset is commonly used in machine learning—both for research and practical applications. Let's dive deeper into the COCO dataset and its significance for computer vision tasks.
--dataset_split_name=train \ --model_name=ssd_300_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --save_summaries_secs=60 \ --save_interval_secs=600 \ --weight_decay=0.0005 \ --optimizer=adam \ --learning_rate=0.001 \ --batch_size=32 \ ...
root = r"D:\dataset\belt\JPEGImages" output = r"D:\dataset\belt\ImageSets\Segmentation" filename = [] #从存放原图的目录中遍历所有图像文件 # dirs = os.listdir(root) for root, dir, files in os.walk(root): for file in files:
parser=argparse.ArgumentParser(description='convert object label')parser.add_argument('data',metavar='DIR',help='path to dataset')parser.add_argument('keyframe_dir',metavar='DIR',help='path to frame dir')parser.add_argument('--mode',type=str,choices=['train','val','test'])args=parser....