Mmdetection: I get the error which is KeyError: 'area' when I run the test.py
python tools/test.py configs/faster_rcnn_r50_fpn_1x.py weights/epoch_500.pth --out ./result/result_500.pkl --eval bbox --show
and it show:
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 200/200, 4.5 task/s, elapsed: 45s, ETA: 0s
writing results to ./result/result_500.pkl
Starting evaluate bbox
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
Traceback (most recent call last):
File "tools/test.py", line 197, in
main()
File "tools/test.py", line 186, in main
coco_eval(result_file, eval_types, dataset.coco)
File "/home/hhm/anaconda3/envs/pytorch/lib/python3.6/site-packages/mmdet-0.6.0+unknown-py3.6.egg/mmdet/core/evaluation/coco_utils.py", line 36, in coco_eval
cocoEval.evaluate()
File "/home/hhm/anaconda3/envs/pytorch/lib/python3.6/site-packages/pycocotools-2.0.0-py3.6-linux-x86_64.egg/pycocotools/cocoeval.py", line 156, in evaluate
for catId in catIds
File "/home/hhm/anaconda3/envs/pytorch/lib/python3.6/site-packages/pycocotools-2.0.0-py3.6-linux-x86_64.egg/pycocotools/cocoeval.py", line 158, in
for imgId in p.imgIds
File "/home/hhm/anaconda3/envs/pytorch/lib/python3.6/site-packages/pycocotools-2.0.0-py3.6-linux-x86_64.egg/pycocotools/cocoeval.py", line 252, in evaluateImg
if g['ignore'] or (g['area']
i can't get the AP and AR,so what is wrong?
hhm853610070
All 5 comments
The annotation is not correctly converted to COCO-style.
hellock
on 17 Jun 2019
I am getting the same error and I cannot find the error with the converted COCO-style annotation. Here is a sample of my annotation file:
{
"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
}
]
}
pittyacg
on 13 Nov 2019
@pittyacg did you find the solution to your problem, I have a very similar annotation file without "area" field. I don't have this "area" field for my images.
How to solve this issue?
Rajat-Mehta
on 14 Jun 2020
Just add the 'area'（=width*height） key for every image.
PanJianning
on 15 Jun 2020
According to the description of coco dataset, 'area' key represents the area covered by pixels belonging to segmented object. It has nothing to do with the 'detection' annotations. So why do we need 'area' term for detection task ?
Rajat-Mehta
on 15 Jun 2020
Related issues
namheegordonkim
·
3Comments
michaelisc
·
3Comments
Youngkl0726
·
3Comments
BeBeauty
·
3Comments
songyuc
·
3Comments
Most helpful comment
According to the description of coco dataset, 'area' key represents the area covered by pixels belonging to segmented object. It has nothing to do with the 'detection' annotations. So why do we need 'area' term for detection task ?