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detectron-cascade-rcnn-exp

2020-03-05 |

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detectron-cascade-rcnn-exp

detectron-cascadee-exp

Experiment results of implement Cascade RCNN under Detectron. Using ResNet50 as feature extractor, as well as 1x iterations. Learning rate start as 0.01 (4GPU, 2 images per GPU).

Communication

All the experiments I have tried are shown as below, but the results are not as expected, any ideas and suggestions helpful are welcomed.

Using statement

Folder detectron_cascade are codes to implement Cascade RCNN under Detectron, parallelizing with folder $Detectron/detcectron.

Folder configs/cascade/ contains yaml files conducting the Cascade RCNN model training.

MSCOCO experiments

mask iterative bbox rcnn results (using same IOU threshold in three stage of RCNN)

model is trained on coco2017train + val

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large	mask_ap	mask_ap50	mask_ap75	mask_ap_small	mask_ap_med	mask_ap_large
mask-R50	test-dev	38.2%	60.05	41.5%	21.8%	40.3%	48.4%	34.3%	56.5%	36.3%	14.9%	36.1%	49.7%
cascade stage1	test-dev	38.3%						34.2%
cascade stage2	test-dev	38.9%						34.1%
cascade stage3	test-dev	38.9%	59.5%	42.1%	21.5%	40.7%	50.2%	34.0%	56.1%	35.9%	14.8%	35.5%	49.5%
cascade stage 1~2	test-dev
cascade stage 1~3	test-dev

mask cascade rcnn results beta version 1

(clip bbox and add invalid bbox check in DecodeBBoxOp)

model is trained on coco2017train + val

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large	mask_ap	mask_ap50	mask_ap75	mask_ap_small	mask_ap_med	mask_ap_large
mask-R50	test-dev	38.2%	60.05	41.5%	21.8%	40.3%	48.4%	34.3%	56.5%	36.3%	14.9%	36.1%	49.7%
cascade stage1	test-dev	38.2%	59.9%	41.7%	21.7%	40.4%	48.4%	34.2%	56.4%	36.1%	15.0%	36.0%	49.5%
cascade stage2	test-dev	38.1%	58.5%	41.3%	18.2%	39.7%	53.4%	34.7%	56.5%	36.8%	15.1%	36.5%	50.6%
cascade stage3	test-dev	39.4%	57.5%	43.5%	21.4%	41.2%	51.1%	34.2%	55.0%	36.4%	14.7%	35.9%	49.9%
cascade stage 1~2	test-dev
cascade stage 1~3	test-dev

mask cascade rcnn results beta version 2

(screen out high iou boxes in DecodeBBoxOp)

model is trained on coco2017train

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large	mask_ap	mask_ap50	mask_ap75	mask_ap_small	mask_ap_med	mask_ap_large
mask-R50	test-dev	38.6%						34.5%
cascade stage1	test-dev
cascade stage2	test-dev
cascade stage3	test-dev	39.06%	56.98%	43.28%	21.86%	41.54%	52.41%	34.20%	54.47%	36.65%	15.11%	36.47%	51.51%
cascade stage 1~2	test-dev
cascade stage 1~3	test-dev

mask cascade rcnn results beta version 3

(add weight to rcnn loss)

model is trained on coco2017train

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large	mask_ap
mask-R50	test-dev	38.0%						34.5%
cascade stage1	test-dev
cascade stage2	test-dev
cascade stage3	test-dev	38.5%	57.2%	42.7%	20.9%	40.7%	49.1%
cascade stage 1~2	test-dev
cascade stage 1~3	test-dev

mask cascade rcnn results beta version 4

(use cls_agnostic_bbox_reg、specific lr_mult)

model is trained on coco2017train

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large	mask_ap	mask_ap50	mask_ap75	mask_ap_small	mask_ap_med	mask_ap_large
mask-R50	test-dev(val)	38.00%(37.7%)	59.7%	41.3%	21.2%	40.2%	48.1%	34.20%(33.9%)	56.4%	36.0%	14.8%	36.0%	49.7%
cascade stage1	test-dev	36.8%	58.1%	40.0%	20.3%	39.0%	47.2%	33.5%	54.9%	35.4%	14.3%	35.2%	48.2%
cascade stage2	test-dev	38.9%	58.6%	42.8%	21.0%	40.9%	50.5%	34.4%	55.6%	36.6%	14.5%	36.0%	50.2%
cascade stage3	test-dev	38.9%	57.4%	43.1%	20.8%	40.8%	51.0%	34.3%	54.7%	36.7%	14.4%	35.8%	50.0%
cascade stage 1~2	test-dev	38.9%	59.0%	42.7%	21.3%	41.0%	50.5%	34.4%	55.8%	36.5%	14.6%	36.0%	50.3%
cascade stage 1~3	test-dev(val)	39.50%(39.14%)	58.90%(58.36%)	43.40%(42.85%)	21.50%(21.41%)	41.40%(41.52%)	51.30%(53.03%)	34.60%(34.37%)	55.80%(55.22%)	36.80%(36.57%)	14.80%(15.17%)	36.20%(36.5%)	50.40%(52.09%)

mask cascade rcnn results beta version 4 large iter

model is trained on coco2017train, learning rate start at 0.01, reduce to 0.001 at 160000 iterations and 0.0001 at 240000 iterations

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large	mask_ap	mask_ap50	mask_ap75	mask_ap_small	mask_ap_med	mask_ap_large
cascade stage 1~3	test-dev(val)	40.10%(39.75%)	59.40%(58.91%)	43.90%(43.56%)	22.00%(21.78%)	41.90%(42.13%)	51.90%(54.24%)	35.00%(34.73%)	56.30%(55.82%)	37.20%(36.90%)	15.10%(14.85%)	36.60%(36.93%)	51.00%(53.20%)

faster cascade rcnn results

model is trained on coco2017train

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large
faster-FPN-R50	test-dev(val)	(36.7%)	(58.45%)	(39.61%)	(21.12%)	(39.85%)	(48.13%)
cascade stage1	test-dev(val)
cascade stage2	test-dev(val)
cascade stage3	test-dev(val)
cascade stage 1~2	test-dev(val)
cascade stage 1~3	test-dev(val)	(37.31%)	(55.51%)	(40.65%)	(20.30%)	(39.87%)	(49.21%)

PASCAL VOC experiments

model is trained on voc0712 trainval, tested on voc2007 test, using coco evaluation metrics

experiments	dataset	box_ap	box_ap50	box_ap75	box_ap_small	box_ap_med	box_ap_large
faster-FPN-R50	voc2007_val	46.75%	77.06%	50.32%	16.54%	35.10%	54.36%
cascade stage1	voc2007_test	36.80%	71.74%	32.66%	12.88%	28.62%	42.55%
cascade stage2	voc2007_test	46.61%	74.41%	50.68%	16.44%	33.90%	54.52%
cascade stage3	voc2007_test	47.50%	73.03%	52.19%	15.93%	34.66%	55.38%
cascade stage 1~2	voc2007_test	47.20%	75.40%	51.35%	16.45%	34.68%	55.06%
cascade stage 1~3	voc2007_test	48.75%	75.40%	53.24%	16.93%	35.56%	56.82%

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