Comments (13)
fanq15
commented on August 16, 2024
Could send me your training log?
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zr526799544
commented on August 16, 2024
Could send me your training log?
I do not train the model I just test it on coco val use the weight you provided.
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zr526799544
commented on August 16, 2024
Could send me your training log?
the code I run has been send to you email.
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fanq15
commented on August 16, 2024
What's your detectron2 version?
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zr526799544
commented on August 16, 2024
What's your detectron2 version?
0.3
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fanq15
commented on August 16, 2024
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
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zr526799544
commented on August 16, 2024
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.
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zr526799544
commented on August 16, 2024
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'
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fanq15
commented on August 16, 2024
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
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zr526799544
commented on August 16, 2024
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?
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zr526799544
commented on August 16, 2024
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
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zr526799544
commented on August 16, 2024
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
after motify the result I can get below.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.019
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.019
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 ] = 0.009
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.041
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.036
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.041
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.041
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 ] = 0.013
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.096
[12/31 01:46:01 fewx.evaluation.coco_evaluation]: Evaluation results for bbox:
| 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 |
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Zhang1Sheng
commented on August 16, 2024
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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Related Issues (20)
- confused about `dataset_dict` in `FewX/fewx/data/dataset_mapper.py` HOT 1
- ============ Few-shot object detetion will start. ============= HOT 3
- How to train an own model
- ValueError: Unsupported type found in checkpoint! res4_avg: <class 'dict'> HOT 1
- About query images and support images HOT 2
- random seed
- RuntimeErroe HOT 1
- Is FSVOD code available? HOT 2
- Can I train directly with VOC dataset?
- 元学习or两阶段微调 HOT 1
- About the test result
- KeyError: 'id' HOT 1
- code of cpmask
- Low performances/Wrong boxes fix
- Thanks for your great work, could you please release the code of your paper《Few-Shot Object Detection with Model Calibration》??
- The question about the 'first_stride' HOT 3
- RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:32, unhandled cuda error, NCCL version 2.4.8
- episodes的训练策略
- FSVOD dataset is incomplete HOT 1
- How to understand the instruction in the step 3 when i have only 2 gpus? HOT 1
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