Python Coco类型数据集返回错误的评估
我使用的是来自美国的更快的R-CNN模型。在将KITTI数据集转换为Coco格式（2D对象检测）后，我尝试评估训练模型的结果 结果是0或-1，有时它会在CocoApi工具包中的g[“area”]处抛出一个错误 如果g['ignore']或（g['area']aRng[1]），则为pycoco： “KeyError:'区域'” 从我在研究问题时发现的情况来看，“区域”用于分割，我的数据集中没有这种注释 我转换的注释文件外观的一个小示例：Python Coco类型数据集返回错误的评估,python,object-detection,pycocotools,Python,Object Detection,Pycocotools,我使用的是来自美国的更快的R-CNN模型。在将KITTI数据集转换为Coco格式（2D对象检测）后，我尝试评估训练模型的结果 结果是0或-1，有时它会在CocoApi工具包中的g[“area”]处抛出一个错误 如果g['ignore']或（g['area']aRng[1]），则为pycoco： “KeyError:'区域'” 从我在研究问题时发现的情况来看，“区域”用于分割，我的数据集中没有这种注释 我转换的注释文件外观的一个小示例： { "images": [ {
{
"images": [
{
"file_name": "007292.png",
"id": 1,
"width": 1392,
"height": 512
},
{
"file_name": "000603.png",
"id": 2,
"width": 1392,
"height": 512
},
{
"file_name": "004313.png",
"id": 3,
"width": 1392,
"height": 512
},
{
"file_name": "006401.png",
"id": 4,
"width": 1392,
"height": 512
},
{
"file_name": "005442.png",
"id": 5,
"width": 1392,
"height": 512
}
],
"annotations": [
{
"image_id": 1,
"id": 1,
"category_id": 1,
"bbox": [
589.08,
176.53,
26.719999999999914,
26.409999999999997
],
"iscrowd": 0
},
{
"image_id": 1,
"id": 2,
"category_id": 1,
"bbox": [
235.9,
190.63,
115.16,
57.78
],
"iscrowd": 0
},
{
"image_id": 1,
"id": 3,
"category_id": 1,
"bbox": [
426.57,
184.2,
42.0,
26.700000000000017
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 4,
"category_id": 1,
"bbox": [
1211.2,
182.65,
11.799999999999955,
186.35
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 5,
"category_id": 1,
"bbox": [
386.94,
180.98,
57.80000000000001,
30.55000000000001
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 6,
"category_id": 1,
"bbox": [
736.21,
173.49,
113.90999999999997,
96.44999999999999
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 7,
"category_id": 1,
"bbox": [
701.98,
174.7,
91.55999999999995,
66.01000000000002
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 8,
"category_id": 1,
"bbox": [
682.42,
176.25,
58.200000000000045,
47.53
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 9,
"category_id": 1,
"bbox": [
667.8,
175.85,
51.190000000000055,
39.24000000000001
],
"iscrowd": 0
},
{
"image_id": 2,
"id": 10,
"category_id": 1,
"bbox": [
654.6,
176.88,
31.110000000000014,
26.49000000000001
],
"iscrowd": 0
},
{
"image_id": 3,
"id": 11,
"category_id": 1,
"bbox": [
267.69,
179.7,
101.13,
33.120000000000005
],
"iscrowd": 0
},
{
"image_id": 3,
"id": 12,
"category_id": 1,
"bbox": [
461.31,
176.05,
72.38000000000005,
28.73999999999998
],
"iscrowd": 0
},
{
"image_id": 3,
"id": 13,
"category_id": 1,
"bbox": [
600.36,
177.08,
52.360000000000014,
23.299999999999983
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 14,
"category_id": 1,
"bbox": [
1061.94,
96.68,
179.05999999999995,
277.32
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 15,
"category_id": 1,
"bbox": [
280.52,
184.02,
148.01,
96.92999999999998
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 16,
"category_id": 1,
"bbox": [
143.54,
179.75,
350.11,
194.25
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 17,
"category_id": 1,
"bbox": [
861.45,
139.2,
178.20000000000005,
64.58000000000001
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 18,
"category_id": 1,
"bbox": [
1018.27,
144.44,
88.04999999999995,
43.25
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 19,
"category_id": 1,
"bbox": [
1061.23,
147.01,
100.31999999999994,
39.27000000000001
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 20,
"category_id": 1,
"bbox": [
439.12,
184.57,
66.10000000000002,
43.43000000000001
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 21,
"category_id": 1,
"bbox": [
381.68,
184.81,
98.5,
63.59
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 22,
"category_id": 1,
"bbox": [
673.9,
172.28,
52.389999999999986,
36.53999999999999
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 23,
"category_id": 1,
"bbox": [
473.3,
180.94,
49.079999999999984,
36.900000000000006
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 24,
"category_id": 1,
"bbox": [
609.73,
179.26,
35.860000000000014,
27.670000000000016
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 25,
"category_id": 1,
"bbox": [
668.0,
173.81,
88.37,
31.150000000000006
],
"iscrowd": 0
},
{
"image_id": 4,
"id": 26,
"category_id": 1,
"bbox": [
585.17,
172.42,
40.520000000000095,
15.630000000000024
],
"iscrowd": 0
},
{
"image_id": 5,
"id": 27,
"category_id": 1,
"bbox": [
192.88,
178.88,
74.23000000000002,
33.49000000000001
],
"iscrowd": 0
},
{
"image_id": 5,
"id": 28,
"category_id": 1,
"bbox": [
250.68,
179.92,
65.70999999999998,
26.27000000000001
],
"iscrowd": 0
},
{
"image_id": 5,
"id": 29,
"category_id": 1,
"bbox": [
306.54,
178.95,
55.48999999999995,
22.670000000000016
],
"iscrowd": 0
}
],
"categories": [
{
"name": "Car",
"id": 1
}
]
}
地区的AP进行了评估
因此，如果您自己的自定义数据集中没有区域
，则站点包/pycocotools/cocoeval.py的以下代码部分将发生错误
如果g['ignore']或（g['area']aRng[1]）：
此代码表示如果区域超过上限aRng[1]
或小于下限aRng[0]
，则从评估目标中忽略该区域
如果您使用自己的自定义数据集，则地面真相中可能没有区域
。在这种情况下，您可以从边界框的宽度和高度创建一个区域
如果您不需要评估每个区域，您可以像下面的代码那样注释掉。
#它不会对每个区域进行评估
#如果g['ignore']或（g['area']aRng[1]）：
如果g['ignore']：
# It does not evaluate for each area
# if g['ignore'] or (g['area']<aRng[0] or g['area']>aRng[1]):
if g['ignore']: