how to get 12 map？ #28

zr526799544 · 2020-12-27T13:46:12Z

I run all.sh and get nothing. so I use detetron demo predict code to generate the coco result and the resulte is to low.loading annotations into memory...
Done (t=0.50s)
creating index...
index created!
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=11.88s).
Accumulating evaluation results...
DONE (t=2.87s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.011
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.020
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.023
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.027
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.032
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.032
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.010
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.071

fanq15 · 2020-12-28T03:38:28Z

Could send me your training log?

zr526799544 · 2020-12-28T03:43:23Z

Could send me your training log?

I do not train the model I just test it on coco val use the weight you provided.

zr526799544 · 2020-12-28T03:46:31Z

Could send me your training log?

the code I run has been send to you email.

fanq15 · 2020-12-28T03:47:27Z

What's your detectron2 version?

zr526799544 · 2020-12-28T03:49:16Z

What's your detectron2 version?

0.3

fanq15 · 2020-12-28T03:52:21Z

Please try this one to install detectron2 0.1 version

python -m pip install detectron2 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html

zr526799544 · 2020-12-28T09:42:52Z

Please try this one to install detectron2 0.1 version
python -m pip install detectron2 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html

YOU install MD said that recommend the Pre-Built Detectron2 (Linux only) version with pytorch 1.5. I use the Pre-Built Detectron2 with CUDA 10.1 and pytorch 1.5. and I found detectron2 V0.1 just for pytorch 1.4 and use python -m pip install detectron2 -f
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html can just install V0.3.

zr526799544 · 2020-12-28T09:44:13Z

Please try this one to install detectron2 0.1 version
python -m pip install detectron2 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html

I can just use V0.1.3 and meet /workspace/FewX-master/fewx/modeling/fsod/fsod_roi_heads.py in ()
8
9 from detectron2.config import configurable
---> 10 from detectron2.layers import ShapeSpec, nonzero_tuple
11 from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
12 from detectron2.utils.events import get_event_storage

ImportError: cannot import name 'nonzero_tuple'

fanq15 · 2020-12-30T10:16:03Z

I tried the evaluation again and I can get the reported number.

[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Preparing results for COCO format ...
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Saving results to ./output/fsod/finetune_dir/R_50_C4_1x/inference/coco_instances_results.json
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Evaluating predictions ...
Loading and preparing results...
DONE (t=0.13s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 10.44 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 1.24 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.030
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.056
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.029
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.052
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.047
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.114
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for bbox: 
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 2.989 | 5.592  | 2.948  | 0.724 | 3.057 | 5.165 |
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP  : 11.95
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP50: 22.37
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP75: 11.79
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APs : 2.89
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APm : 12.23
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APl : 20.66

zr526799544 · 2020-12-30T12:32:57Z

I tried the evaluation again and I can get the reported number.

[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Preparing results for COCO format ...
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Saving results to ./output/fsod/finetune_dir/R_50_C4_1x/inference/coco_instances_results.json
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Evaluating predictions ...
Loading and preparing results...
DONE (t=0.13s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 10.44 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 1.24 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.030
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.056
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.029
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.052
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.047
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.114
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for bbox: 
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 2.989 | 5.592  | 2.948  | 0.724 | 3.057 | 5.165 |
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP  : 11.95
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP50: 22.37
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP75: 11.79
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APs : 2.89
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APm : 12.23
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APl : 20.66

you env is pytorch 1.5 . detectron2 v0.31 ? just run all.sh can get the result?

zr526799544 · 2020-12-31T01:37:48Z

I tried the evaluation again and I can get the reported number.

[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Preparing results for COCO format ...
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Saving results to ./output/fsod/finetune_dir/R_50_C4_1x/inference/coco_instances_results.json
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Evaluating predictions ...
Loading and preparing results...
DONE (t=0.13s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 10.44 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 1.24 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.030
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.056
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.029
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.052
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.047
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.114
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for bbox: 
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 2.989 | 5.592  | 2.948  | 0.724 | 3.057 | 5.165 |
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP  : 11.95
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP50: 22.37
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP75: 11.79
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APs : 2.89
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APm : 12.23
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APl : 20.66

I find the problem..... In your all.sh you do delete the ./support_dir/support_feature.pkl which inference needed ... plz delete it in your all.sh and plz delete the code self.logger.info("===========inference call===========") in your fewx/modeling/fsod/fsod_rcnn.py
340

zr526799544 · 2020-12-31T01:47:19Z

I tried the evaluation again and I can get the reported number.

[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Preparing results for COCO format ...
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Saving results to ./output/fsod/finetune_dir/R_50_C4_1x/inference/coco_instances_results.json
[12/30 18:13:39 fewx.evaluation.coco_evaluation]: Evaluating predictions ...
Loading and preparing results...
DONE (t=0.13s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 10.44 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 1.24 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.030
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.056
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.029
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.052
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.047
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.114
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for bbox: 
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 2.989 | 5.592  | 2.948  | 0.724 | 3.057 | 5.165 |
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP  : 11.95
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP50: 22.37
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP75: 11.79
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APs : 2.89
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APm : 12.23
[12/30 18:13:51 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APl : 20.66

AP	AP50	AP75	APs	APm	APl
1.939	3.469	1.948	0.015	0.889	4.124
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP : 7.75
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP50: 13.88
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> AP75: 7.79
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APs : 0.06
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APm : 3.56
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for VOC 20 categories =======> APl : 16.50

Zhang1Sheng · 2021-04-13T03:25:16Z

I know that the model generates 100 BBoxes for each class, but when I predicted that only dozens of BBoxes were generated for each class. Why not 100 and how to solve this problem?

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how to get 12 map？ #28

how to get 12 map？ #28

zr526799544 commented Dec 27, 2020

fanq15 commented Dec 28, 2020

zr526799544 commented Dec 28, 2020

zr526799544 commented Dec 28, 2020

fanq15 commented Dec 28, 2020

zr526799544 commented Dec 28, 2020

fanq15 commented Dec 28, 2020

zr526799544 commented Dec 28, 2020

zr526799544 commented Dec 28, 2020 •

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fanq15 commented Dec 30, 2020

zr526799544 commented Dec 30, 2020

zr526799544 commented Dec 31, 2020 •

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zr526799544 commented Dec 31, 2020 •

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Zhang1Sheng commented Apr 13, 2021

how to get 12 map？ #28

how to get 12 map？ #28

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