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c6c496f
1 Parent(s): bb15987

update configs

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  1. configs/Base-RCNN-C4.yaml +0 -0
  2. configs/Base-RCNN-DilatedC5.yaml +0 -0
  3. configs/Base-RCNN-FPN-4gpu.yaml +0 -44
  4. configs/Base-RCNN-FPN.yaml +2 -1
  5. configs/Base-RetinaNet.yaml +0 -0
  6. configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml +0 -17
  7. configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml +0 -9
  8. configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml +0 -9
  9. configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml +0 -9
  10. configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml +0 -6
  11. configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml +0 -9
  12. configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml +0 -6
  13. configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml +0 -9
  14. configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml +0 -6
  15. configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml +0 -9
  16. configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml +0 -13
  17. configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml +0 -8
  18. configs/COCO-Detection/retinanet_R_50_FPN_1x.py +0 -9
  19. configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml +0 -5
  20. configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml +0 -8
  21. configs/COCO-Detection/rpn_R_50_C4_1x.yaml +0 -10
  22. configs/COCO-Detection/rpn_R_50_FPN_1x.yaml +0 -9
  23. configs/COCO-InstanceSegmentation/.mask_rcnn_R_50_FPN_1x_4gpu.yaml.swp +0 -0
  24. configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml +0 -9
  25. configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml +0 -9
  26. configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x_4gpu_transfiner.yaml +0 -9
  27. configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x_4gpu_transfiner_lvis.yaml +0 -12
  28. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py +0 -7
  29. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml +0 -6
  30. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml +0 -9
  31. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml +0 -6
  32. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml +0 -9
  33. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py +0 -7
  34. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_4gpu.yaml +0 -6
  35. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_4gpu_transfiner.yaml +0 -6
  36. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml +0 -12
  37. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner.yaml +0 -9
  38. configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner_lvis.yaml +0 -12
  39. configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml +0 -13
  40. configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x_transfiner.yaml +0 -13
  41. configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py +0 -34
  42. configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py +0 -35
  43. configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml +0 -15
  44. configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml +0 -8
  45. configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py +0 -7
  46. configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml +0 -5
  47. configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml +0 -8
  48. configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml +0 -12
  49. configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml +0 -11
  50. configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml +0 -8
configs/Base-RCNN-C4.yaml CHANGED
File without changes
configs/Base-RCNN-DilatedC5.yaml CHANGED
File without changes
configs/Base-RCNN-FPN-4gpu.yaml DELETED
@@ -1,44 +0,0 @@
1
- MODEL:
2
- META_ARCHITECTURE: "GeneralizedRCNN"
3
- BACKBONE:
4
- NAME: "build_resnet_fpn_backbone"
5
- RESNETS:
6
- OUT_FEATURES: ["res2", "res3", "res4", "res5"]
7
- FPN:
8
- IN_FEATURES: ["res2", "res3", "res4", "res5"]
9
- ANCHOR_GENERATOR:
10
- SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
11
- ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
12
- RPN:
13
- IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
14
- PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
15
- PRE_NMS_TOPK_TEST: 1000 # Per FPN level
16
- # Detectron1 uses 2000 proposals per-batch,
17
- # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
18
- # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
19
- POST_NMS_TOPK_TRAIN: 1000
20
- POST_NMS_TOPK_TEST: 1000
21
- ROI_HEADS:
22
- NAME: "StandardROIHeads"
23
- IN_FEATURES: ["p2", "p3", "p4", "p5"]
24
- ROI_BOX_HEAD:
25
- NAME: "FastRCNNConvFCHead"
26
- NUM_FC: 2
27
- POOLER_RESOLUTION: 7
28
- ROI_MASK_HEAD:
29
- NAME: "MaskRCNNConvUpsampleHead"
30
- NUM_CONV: 4
31
- POOLER_RESOLUTION: 14
32
- DATASETS:
33
- TRAIN: ("coco_2017_train",)
34
- #TEST: ("coco_2017_val",)
35
- #TEST: ("lvis_v0.5_val_cocofied",)
36
- TEST: ("coco_2017_test-dev",)
37
- SOLVER:
38
- IMS_PER_BATCH: 16 #8 #16
39
- BASE_LR: 0.02 # 0.02
40
- STEPS: (60000, 80000)
41
- MAX_ITER: 90000
42
- INPUT:
43
- MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
44
- VERSION: 2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/Base-RCNN-FPN.yaml CHANGED
@@ -31,7 +31,8 @@ MODEL:
31
  POOLER_RESOLUTION: 14
32
  DATASETS:
33
  TRAIN: ("coco_2017_train",)
34
- TEST: ("coco_2017_val",)
 
35
  SOLVER:
36
  IMS_PER_BATCH: 16 #16
37
  BASE_LR: 0.02
 
31
  POOLER_RESOLUTION: 14
32
  DATASETS:
33
  TRAIN: ("coco_2017_train",)
34
+ #TEST: ("coco_2017_val",)
35
+ TEST: ("coco_2017_test-dev",)
36
  SOLVER:
37
  IMS_PER_BATCH: 16 #16
38
  BASE_LR: 0.02
configs/Base-RetinaNet.yaml CHANGED
File without changes
configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml DELETED
@@ -1,17 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- LOAD_PROPOSALS: True
6
- RESNETS:
7
- DEPTH: 50
8
- PROPOSAL_GENERATOR:
9
- NAME: "PrecomputedProposals"
10
- DATASETS:
11
- TRAIN: ("coco_2017_train",)
12
- PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_train_box_proposals_21bc3a.pkl", )
13
- TEST: ("coco_2017_val",)
14
- PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
15
- DATALOADER:
16
- # proposals are part of the dataset_dicts, and take a lot of RAM
17
- NUM_WORKERS: 2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-DilatedC5.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-DilatedC5.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-DilatedC5.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: False
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml DELETED
@@ -1,13 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- MASK_ON: False
4
- WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
5
- PIXEL_STD: [57.375, 57.120, 58.395]
6
- RESNETS:
7
- STRIDE_IN_1X1: False # this is a C2 model
8
- NUM_GROUPS: 32
9
- WIDTH_PER_GROUP: 8
10
- DEPTH: 101
11
- SOLVER:
12
- STEPS: (210000, 250000)
13
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml DELETED
@@ -1,8 +0,0 @@
1
- _BASE_: "../Base-RetinaNet.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- RESNETS:
5
- DEPTH: 101
6
- SOLVER:
7
- STEPS: (210000, 250000)
8
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/retinanet_R_50_FPN_1x.py DELETED
@@ -1,9 +0,0 @@
1
- from ..common.optim import SGD as optimizer
2
- from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
3
- from ..common.data.coco import dataloader
4
- from ..common.models.retinanet import model
5
- from ..common.train import train
6
-
7
- dataloader.train.mapper.use_instance_mask = False
8
- model.backbone.bottom_up.freeze_at = 2
9
- optimizer.lr = 0.01
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml DELETED
@@ -1,5 +0,0 @@
1
- _BASE_: "../Base-RetinaNet.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- RESNETS:
5
- DEPTH: 50
 
 
 
 
 
 
configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml DELETED
@@ -1,8 +0,0 @@
1
- _BASE_: "../Base-RetinaNet.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- RESNETS:
5
- DEPTH: 50
6
- SOLVER:
7
- STEPS: (210000, 250000)
8
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/rpn_R_50_C4_1x.yaml DELETED
@@ -1,10 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- META_ARCHITECTURE: "ProposalNetwork"
4
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
5
- MASK_ON: False
6
- RESNETS:
7
- DEPTH: 50
8
- RPN:
9
- PRE_NMS_TOPK_TEST: 12000
10
- POST_NMS_TOPK_TEST: 2000
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-Detection/rpn_R_50_FPN_1x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- META_ARCHITECTURE: "ProposalNetwork"
4
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
5
- MASK_ON: False
6
- RESNETS:
7
- DEPTH: 50
8
- RPN:
9
- POST_NMS_TOPK_TEST: 2000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/.mask_rcnn_R_50_FPN_1x_4gpu.yaml.swp DELETED
Binary file (12.3 kB)
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-DilatedC5.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x_4gpu_transfiner.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- WEIGHTS: "./init_weights/model_final_a3ec72.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x_4gpu_transfiner_lvis.yaml DELETED
@@ -1,12 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- WEIGHTS: "./init_weights/model_final_a3ec72.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 101
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
10
- DATASETS:
11
- TEST: ("lvis_v0.5_val_cocofied",)
12
-
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py DELETED
@@ -1,7 +0,0 @@
1
- from ..common.train import train
2
- from ..common.optim import SGD as optimizer
3
- from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
4
- from ..common.data.coco import dataloader
5
- from ..common.models.mask_rcnn_c4 import model
6
-
7
- model.backbone.freeze_at = 2
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-C4.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-DilatedC5.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-DilatedC5.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py DELETED
@@ -1,7 +0,0 @@
1
- from ..common.optim import SGD as optimizer
2
- from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
3
- from ..common.data.coco import dataloader
4
- from ..common.models.mask_rcnn_fpn import model
5
- from ..common.train import train
6
-
7
- model.backbone.bottom_up.freeze_at = 2
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_4gpu.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_4gpu_transfiner.yaml DELETED
@@ -1,6 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- WEIGHTS: "./init_weights/model_final_a54504.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml DELETED
@@ -1,12 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
7
- RPN:
8
- BBOX_REG_LOSS_TYPE: "giou"
9
- BBOX_REG_LOSS_WEIGHT: 2.0
10
- ROI_BOX_HEAD:
11
- BBOX_REG_LOSS_TYPE: "giou"
12
- BBOX_REG_LOSS_WEIGHT: 10.0
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner.yaml DELETED
@@ -1,9 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- WEIGHTS: "./init_weights/model_final_f10217.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner_lvis.yaml DELETED
@@ -1,12 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- WEIGHTS: "./init_weights/model_final_f10217.pkl"
4
- MASK_ON: True
5
- RESNETS:
6
- DEPTH: 50
7
- SOLVER:
8
- STEPS: (210000, 250000)
9
- MAX_ITER: 270000
10
- DATASETS:
11
- TEST: ("lvis_v0.5_val_cocofied",)
12
-
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml DELETED
@@ -1,13 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- MASK_ON: True
4
- WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
5
- PIXEL_STD: [57.375, 57.120, 58.395]
6
- RESNETS:
7
- STRIDE_IN_1X1: False # this is a C2 model
8
- NUM_GROUPS: 32
9
- WIDTH_PER_GROUP: 8
10
- DEPTH: 101
11
- SOLVER:
12
- STEPS: (210000, 250000)
13
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x_transfiner.yaml DELETED
@@ -1,13 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN-4gpu.yaml"
2
- MODEL:
3
- MASK_ON: True
4
- WEIGHTS: "./init_weights/model_final_x101.pkl"
5
- PIXEL_STD: [57.375, 57.120, 58.395]
6
- RESNETS:
7
- STRIDE_IN_1X1: False # this is a C2 model
8
- NUM_GROUPS: 32
9
- WIDTH_PER_GROUP: 8
10
- DEPTH: 101
11
- SOLVER:
12
- STEPS: (210000, 250000)
13
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py DELETED
@@ -1,34 +0,0 @@
1
- from ..common.optim import SGD as optimizer
2
- from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
3
- from ..common.data.coco import dataloader
4
- from ..common.models.mask_rcnn_fpn import model
5
- from ..common.train import train
6
-
7
- from detectron2.config import LazyCall as L
8
- from detectron2.modeling.backbone import RegNet
9
- from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
10
-
11
-
12
- # Replace default ResNet with RegNetX-4GF from the DDS paper. Config source:
13
- # https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9 # noqa
14
- model.backbone.bottom_up = L(RegNet)(
15
- stem_class=SimpleStem,
16
- stem_width=32,
17
- block_class=ResBottleneckBlock,
18
- depth=23,
19
- w_a=38.65,
20
- w_0=96,
21
- w_m=2.43,
22
- group_width=40,
23
- freeze_at=2,
24
- norm="FrozenBN",
25
- out_features=["s1", "s2", "s3", "s4"],
26
- )
27
- model.pixel_std = [57.375, 57.120, 58.395]
28
-
29
- optimizer.weight_decay = 5e-5
30
- train.init_checkpoint = (
31
- "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth"
32
- )
33
- # RegNets benefit from enabling cudnn benchmark mode
34
- train.cudnn_benchmark = True
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py DELETED
@@ -1,35 +0,0 @@
1
- from ..common.optim import SGD as optimizer
2
- from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
3
- from ..common.data.coco import dataloader
4
- from ..common.models.mask_rcnn_fpn import model
5
- from ..common.train import train
6
-
7
- from detectron2.config import LazyCall as L
8
- from detectron2.modeling.backbone import RegNet
9
- from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
10
-
11
-
12
- # Replace default ResNet with RegNetY-4GF from the DDS paper. Config source:
13
- # https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml#L4-L10 # noqa
14
- model.backbone.bottom_up = L(RegNet)(
15
- stem_class=SimpleStem,
16
- stem_width=32,
17
- block_class=ResBottleneckBlock,
18
- depth=22,
19
- w_a=31.41,
20
- w_0=96,
21
- w_m=2.24,
22
- group_width=64,
23
- se_ratio=0.25,
24
- freeze_at=2,
25
- norm="FrozenBN",
26
- out_features=["s1", "s2", "s3", "s4"],
27
- )
28
- model.pixel_std = [57.375, 57.120, 58.395]
29
-
30
- optimizer.weight_decay = 5e-5
31
- train.init_checkpoint = (
32
- "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth"
33
- )
34
- # RegNets benefit from enabling cudnn benchmark mode
35
- train.cudnn_benchmark = True
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml DELETED
@@ -1,15 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- KEYPOINT_ON: True
4
- ROI_HEADS:
5
- NUM_CLASSES: 1
6
- ROI_BOX_HEAD:
7
- SMOOTH_L1_BETA: 0.5 # Keypoint AP degrades (though box AP improves) when using plain L1 loss
8
- RPN:
9
- # Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2.
10
- # 1000 proposals per-image is found to hurt box AP.
11
- # Therefore we increase it to 1500 per-image.
12
- POST_NMS_TOPK_TRAIN: 1500
13
- DATASETS:
14
- TRAIN: ("keypoints_coco_2017_train",)
15
- TEST: ("keypoints_coco_2017_val",)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml DELETED
@@ -1,8 +0,0 @@
1
- _BASE_: "Base-Keypoint-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- RESNETS:
5
- DEPTH: 101
6
- SOLVER:
7
- STEPS: (210000, 250000)
8
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py DELETED
@@ -1,7 +0,0 @@
1
- from ..common.optim import SGD as optimizer
2
- from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
3
- from ..common.data.coco_keypoint import dataloader
4
- from ..common.models.keypoint_rcnn_fpn import model
5
- from ..common.train import train
6
-
7
- model.backbone.bottom_up.freeze_at = 2
 
 
 
 
 
 
 
 
configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml DELETED
@@ -1,5 +0,0 @@
1
- _BASE_: "Base-Keypoint-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- RESNETS:
5
- DEPTH: 50
 
 
 
 
 
 
configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml DELETED
@@ -1,8 +0,0 @@
1
- _BASE_: "Base-Keypoint-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
4
- RESNETS:
5
- DEPTH: 50
6
- SOLVER:
7
- STEPS: (210000, 250000)
8
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml DELETED
@@ -1,12 +0,0 @@
1
- _BASE_: "Base-Keypoint-RCNN-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
4
- PIXEL_STD: [57.375, 57.120, 58.395]
5
- RESNETS:
6
- STRIDE_IN_1X1: False # this is a C2 model
7
- NUM_GROUPS: 32
8
- WIDTH_PER_GROUP: 8
9
- DEPTH: 101
10
- SOLVER:
11
- STEPS: (210000, 250000)
12
- MAX_ITER: 270000
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml DELETED
@@ -1,11 +0,0 @@
1
- _BASE_: "../Base-RCNN-FPN.yaml"
2
- MODEL:
3
- META_ARCHITECTURE: "PanopticFPN"
4
- MASK_ON: True
5
- SEM_SEG_HEAD:
6
- LOSS_WEIGHT: 0.5
7
- DATASETS:
8
- TRAIN: ("coco_2017_train_panoptic_separated",)
9
- TEST: ("coco_2017_val_panoptic_separated",)
10
- DATALOADER:
11
- FILTER_EMPTY_ANNOTATIONS: False
 
 
 
 
 
 
 
 
 
 
 
 
configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml DELETED
@@ -1,8 +0,0 @@
1
- _BASE_: "Base-Panoptic-FPN.yaml"
2
- MODEL:
3
- WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
4
- RESNETS:
5
- DEPTH: 101
6
- SOLVER:
7
- STEPS: (210000, 250000)
8
- MAX_ITER: 270000