PaddlePaddle / PaddleDetection

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
Apache License 2.0
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PPYOLOE+在推理的时候保存bbox.json,为什么category_id能大于80(如图所示)?COCO训练不是80类么? #7103

Closed lixiangMindSpore closed 1 year ago

lixiangMindSpore commented 1 year ago

问题确认 Search before asking

请提出你的问题 Please ask your question

image PPYOLOE+在推理的时候保存bbox.json,为什么category_id能大于80(如图所示)?COCO训练不是80类么?category_id对应的类别名称在哪个文件里?

wangxinxin08 commented 1 year ago

category_id是coco的定义,建议看下coco标注格式的定义

lixiangMindSpore commented 1 year ago

category_id是coco的定义,建议看下coco标注格式的定义

COCO也就80类,为什么用官网模型(PPYOLOE-S+)能测出80类以外的类别?

wangxinxin08 commented 1 year ago

coco的category_id和class_id不同,class_id是我们自己定义的0-79,category_id是coco对类别打的顺序,为什么连coco的定义都不知道

lixiangMindSpore commented 1 year ago

coco的category_id和class_id不同,class_id是我们自己定义的0-79,category_id是coco对类别打的顺序,为什么连coco的定义都不知道

谢谢您的解答,但是不要带情绪,我知道COCO格式,你们自己写的category.py自定义了0-80(不是0-79,还有背景类),说一下就好,都是学习的过程,不懂难道不可以问么?

wangxinxin08 commented 1 year ago

coco的category_id和class_id不同,class_id是我们自己定义的0-79,category_id是coco对类别打的顺序,为什么连coco的定义都不知道

谢谢您的解答,但是不要带情绪,我知道COCO格式,你们自己写的category.py自定义了0-80(不是0-79,还有背景类),说一下就好,都是学习的过程,不懂难道不可以问么?

先问是不是,再问为什么,不要张口就来,有些问题自己先想一想,可以节省大家的时间,已经建议你去看coco的标注格式,还问出同样的问题实在不应该

h030162 commented 1 year ago

{1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', 41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon', 51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', 86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}

lixiangMindSpore commented 1 year ago

{1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', 41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon', 51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', 86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}

谢谢,已了解