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could you provide V19-Lite trained models for Panoptic segmentation #54

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griffintin opened this issue Aug 7, 2020 · 3 comments
Open

could you provide V19-Lite trained models for Panoptic segmentation #54

griffintin opened this issue Aug 7, 2020 · 3 comments

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@griffintin
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Hi, @youngwanLEE

Thank you so much for sharing this amazing work.
I am about to evaluate centermask for Panoptic segmentation, and mainly focused on low-power consumption models.

Is it possible to share models trained on V19-Lite-Slim architecture for Panoptic-Segmentation?
V39 is really good, but i am wondering for a more compact network.

Thank you for your time and hopping to hearing from you.
@griffintin
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griffintin commented Aug 24, 2020 •
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@youngwanLEE
hello, I trained V19-Lite-Slim for Panoptic-Segmentation, However, accuracy for mask AP is 24.2, bbox AP is 26.7 and PQ is 32.02. Since there is no provided data in your Git or in the paper for this architecture, I refer to mask AP in instance segmentation using the same architecture, which is 29.80.

you can see, mask AP in my own panoptic training is not as good as that in your provided instance segmentation. I have no idea why, can you share any ideas.

By the way, I disabled mask_scoring, which takes too many parameters.

Here is the config file:

CUDNN_BENCHMARK: false
DATALOADER:
ASPECT_RATIO_GROUPING: true
FILTER_EMPTY_ANNOTATIONS: true
NUM_WORKERS: 4
REPEAT_THRESHOLD: 0.0
SAMPLER_TRAIN: TrainingSampler
DATASETS:
PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
PROPOSAL_FILES_TEST: []
PROPOSAL_FILES_TRAIN: []
TEST:

  • coco_2017_val_panoptic_separated
    TRAIN:
  • coco_2017_train_panoptic_separated
    GLOBAL:
    HACK: 1.0
    INPUT:
    CROP:
    ENABLED: false
    SIZE:
    • 0.9
    • 0.9
      TYPE: relative_range
      FORMAT: BGR
      MASK_FORMAT: polygon
      MAX_SIZE_TEST: 1000
      MAX_SIZE_TRAIN: 1000
      MIN_SIZE_TEST: 600
      MIN_SIZE_TRAIN:
  • 580
  • 600
    MIN_SIZE_TRAIN_SAMPLING: choice
    MODEL:
    ANCHOR_GENERATOR:
    ANGLES:
      • -90
      • 0
      • 90
        ASPECT_RATIOS:
      • 0.5
      • 1.0
      • 2.0
        NAME: DefaultAnchorGenerator
        OFFSET: 0.0
        SIZES:
      • 32
      • 64
      • 128
      • 256
      • 512
        BACKBONE:
        FREEZE_AT: 0
        NAME: build_fcos_vovnet_fpn_backbone
        DEVICE: cuda
        FCOS:
        CENTER_SAMPLE: true
        FPN_STRIDES:
    • 8
    • 16
    • 32
    • 64
    • 128
      INFERENCE_TH_TEST: 0.05
      INFERENCE_TH_TRAIN: 0.05
      IN_FEATURES:
    • p3
    • p4
    • p5
    • p6
    • p7
      LOC_LOSS_TYPE: giou
      LOSS_ALPHA: 0.25
      LOSS_GAMMA: 2.0
      NMS_TH: 0.6
      NORM: GN
      NUM_BOX_CONVS: 2
      NUM_CLASSES: 80
      NUM_CLS_CONVS: 2
      NUM_SHARE_CONVS: 0
      POST_NMS_TOPK_TEST: 50
      POST_NMS_TOPK_TRAIN: 100
      POS_RADIUS: 1.5
      PRE_NMS_TOPK_TEST: 1000
      PRE_NMS_TOPK_TRAIN: 1000
      PRIOR_PROB: 0.01
      SIZES_OF_INTEREST:
    • 64
    • 128
    • 256
    • 512
      THRESH_WITH_CTR: false
      TOP_LEVELS: 2
      USE_DEFORMABLE: false
      USE_RELU: true
      USE_SCALE: true
      FPN:
      FUSE_TYPE: sum
      IN_FEATURES:
    • stage2
    • stage3
    • stage4
    • stage5
      NORM: ''
      OUT_CHANNELS: 128
      KEYPOINT_ON: false
      LOAD_PROPOSALS: false
      MASKIOU_LOSS_WEIGHT: 1.0
      MASKIOU_ON: false
      MASK_ON: true
      META_ARCHITECTURE: PanopticFPN
      MOBILENET: false
      PANOPTIC_FPN:
      COMBINE:
      ENABLED: true
      INSTANCES_CONFIDENCE_THRESH: 0.5
      OVERLAP_THRESH: 0.5
      STUFF_AREA_LIMIT: 4096
      INSTANCE_LOSS_WEIGHT: 1.0
      PIXEL_MEAN:
  • 103.53
  • 116.28
  • 123.675
    PIXEL_STD:
  • 1.0
  • 1.0
  • 1.0
    PROPOSAL_GENERATOR:
    MIN_SIZE: 0
    NAME: FCOS
    RESNETS:
    DEFORM_MODULATED: false
    DEFORM_NUM_GROUPS: 1
    DEFORM_ON_PER_STAGE:
    • false
    • false
    • false
    • false
      DEPTH: 50
      NORM: FrozenBN
      NUM_GROUPS: 1
      OUT_FEATURES:
    • res4
      RES2_OUT_CHANNELS: 256
      RES5_DILATION: 1
      STEM_OUT_CHANNELS: 64
      STRIDE_IN_1X1: true
      WIDTH_PER_GROUP: 64
      RETINANET:
      BBOX_REG_WEIGHTS:
    • 1.0
    • 1.0
    • 1.0
    • 1.0
      FOCAL_LOSS_ALPHA: 0.25
      FOCAL_LOSS_GAMMA: 2.0
      IN_FEATURES:
    • p3
    • p4
    • p5
    • p6
    • p7
      IOU_LABELS:
    • 0
    • -1
    • 1
      IOU_THRESHOLDS:
    • 0.4
    • 0.5
      NMS_THRESH_TEST: 0.5
      NUM_CLASSES: 80
      NUM_CONVS: 4
      PRIOR_PROB: 0.01
      SCORE_THRESH_TEST: 0.05
      SMOOTH_L1_LOSS_BETA: 0.1
      TOPK_CANDIDATES_TEST: 1000
      ROI_BOX_CASCADE_HEAD:
      BBOX_REG_WEIGHTS:
      • 10.0
      • 10.0
      • 5.0
      • 5.0
      • 20.0
      • 20.0
      • 10.0
      • 10.0
      • 30.0
      • 30.0
      • 15.0
      • 15.0
        IOUS:
    • 0.5
    • 0.6
    • 0.7
      ROI_BOX_HEAD:
      BBOX_REG_WEIGHTS:
    • 10.0
    • 10.0
    • 5.0
    • 5.0
      CLS_AGNOSTIC_BBOX_REG: false
      CONV_DIM: 256
      FC_DIM: 1024
      NAME: ''
      NORM: ''
      NUM_CONV: 0
      NUM_FC: 0
      POOLER_RESOLUTION: 14
      POOLER_SAMPLING_RATIO: 0
      POOLER_TYPE: ROIAlignV2
      SMOOTH_L1_BETA: 0.0
      TRAIN_ON_PRED_BOXES: false
      ROI_HEADS:
      BATCH_SIZE_PER_IMAGE: 512
      IN_FEATURES:
    • p3
    • p4
    • p5
      IOU_LABELS:
    • 0
    • 1
      IOU_THRESHOLDS:
    • 0.5
      NAME: CenterROIHeads
      NMS_THRESH_TEST: 0.5
      NUM_CLASSES: 80
      POSITIVE_FRACTION: 0.25
      PROPOSAL_APPEND_GT: true
      SCORE_THRESH_TEST: 0.05
      ROI_KEYPOINT_HEAD:
      ASSIGN_CRITERION: ratio
      CONV_DIMS:
    • 512
    • 512
    • 512
    • 512
    • 512
    • 512
    • 512
    • 512
      IN_FEATURES:
    • p2
    • p3
    • p4
    • p5
      LOSS_WEIGHT: 1.0
      MIN_KEYPOINTS_PER_IMAGE: 1
      NAME: KRCNNConvDeconvUpsampleHead
      NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
      NUM_KEYPOINTS: 17
      POOLER_RESOLUTION: 14
      POOLER_SAMPLING_RATIO: 0
      POOLER_TYPE: ROIAlignV2
      ROI_MASKIOU_HEAD:
      CONV_DIM: 128
      NAME: MaskIoUHead
      NUM_CONV: 2
      ROI_MASK_HEAD:
      ASSIGN_CRITERION: ratio
      CLS_AGNOSTIC_MASK: false
      CONV_DIM: 128
      NAME: SpatialAttentionMaskHead
      NORM: ''
      NUM_CONV: 2
      POOLER_RESOLUTION: 14
      POOLER_SAMPLING_RATIO: 0
      POOLER_TYPE: ROIAlignV2
      RPN:
      BATCH_SIZE_PER_IMAGE: 256
      BBOX_REG_WEIGHTS:
    • 1.0
    • 1.0
    • 1.0
    • 1.0
      BOUNDARY_THRESH: -1
      HEAD_NAME: StandardRPNHead
      IN_FEATURES:
    • res4
      IOU_LABELS:
    • 0
    • -1
    • 1
      IOU_THRESHOLDS:
    • 0.3
    • 0.7
      LOSS_WEIGHT: 1.0
      NMS_THRESH: 0.7
      POSITIVE_FRACTION: 0.5
      POST_NMS_TOPK_TEST: 1000
      POST_NMS_TOPK_TRAIN: 2000
      PRE_NMS_TOPK_TEST: 6000
      PRE_NMS_TOPK_TRAIN: 12000
      SMOOTH_L1_BETA: 0.0
      SEM_SEG_HEAD:
      COMMON_STRIDE: 4
      CONVS_DIM: 128
      IGNORE_VALUE: 255
      IN_FEATURES:
    • p2
    • p3
    • p4
    • p5
      LOSS_WEIGHT: 0.5
      NAME: SemSegFPNHead
      NORM: GN
      NUM_CLASSES: 54
      VOVNET:
      BACKBONE_OUT_CHANNELS: 256
      CONV_BODY: V-19-slim-eSE
      DEFORMABLE_GROUPS: 1
      NORM: FrozenBN
      OUT_CHANNELS: 256
      OUT_FEATURES:
    • stage2
    • stage3
    • stage4
    • stage5
      STAGE_WITH_DCN:
    • false
    • false
    • false
    • false
      WITH_MODULATED_DCN: false
      WEIGHTS: output/Panoptic-CenterMask-V-19-slim-eSE-FPN-ms-3x/model_final.pth
      OUTPUT_DIR: output/Panoptic-CenterMask-V-19-slim-eSE-FPN-ms-3x
      SEED: -1
      SOLVER:
      BASE_LR: 0.005
      BIAS_LR_FACTOR: 1.0
      CHECKPOINT_PERIOD: 10000
      CLIP_GRADIENTS:
      CLIP_TYPE: value
      CLIP_VALUE: 1.0
      ENABLED: false
      NORM_TYPE: 2.0
      GAMMA: 0.1
      IMS_PER_BATCH: 8
      LR_SCHEDULER_NAME: WarmupMultiStepLR
      MAX_ITER: 720000
      MOMENTUM: 0.9
      NESTEROV: false
      STEPS:
  • 500000
  • 600000
    WARMUP_FACTOR: 0.001
    WARMUP_ITERS: 1000
    WARMUP_METHOD: linear
    WEIGHT_DECAY: 0.0001
    WEIGHT_DECAY_BIAS: 0.0001
    WEIGHT_DECAY_NORM: 0.0
    TEST:
    AUG:
    ENABLED: false
    FLIP: true
    MAX_SIZE: 4000
    MIN_SIZES:
    • 400
    • 500
    • 600
    • 700
    • 800
    • 900
    • 1000
    • 1100
    • 1200
      DETECTIONS_PER_IMAGE: 100
      EVAL_PERIOD: 0
      EXPECTED_RESULTS: []
      KEYPOINT_OKS_SIGMAS: []
      PRECISE_BN:
      ENABLED: false
      NUM_ITER: 200
      VERSION: 2
      VIS_PERIOD: 0

@griffintin
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@youngwanLEE

I think the results in the table of Panoptic-CenterMask are wrong.
The data for box AP and mask AP are opposite.

@abhigoku10
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@griffintin can you please share ur model on the onedrive or google drive ? it would be helpfull for panoptic segmentation

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