test on pascal voc dataset?

[zimenglan-sysu-512]

hi @unsky,

have u tested focal loss in pascal voc dataset? btw, can u share your parameters? like the hyperparamter in solver.prototxt and some parameters of rpn and fast rcnn?

thanks.

[unsky]

@zimenglan-sysu-512 ---
mxnet (faster rcnn) parameters:

MXNET_VERSION: "mxnet"
output_path: "./output/rcnn/kitti"
symbol: resnet_v1_101_rcnn_dcn
gpus: '1,2,3,4,5,6,7'
CLASS_AGNOSTIC: false
SCALES:

• 740

• 2448
  default:
  frequent: 100
  kvstore: device
  network:
  pretrained: "./model/pretrained_model/resnet_v1_101"
  pretrained_epoch: 0
  PIXEL_MEANS:

  • 103.06
  • 115.90
  • 123.15
    IMAGE_STRIDE: 0
    RCNN_FEAT_STRIDE: 16
    RPN_FEAT_STRIDE: 16
    FIXED_PARAMS:
  • conv1
  • bn_conv1
  • res2
  • bn2
  • gamma
  • beta
    FIXED_PARAMS_SHARED:
  • conv1
  • bn_conv1
  • res2
  • bn2
  • res3
  • bn3
  • res4
  • bn4
  • gamma
  • beta
    ANCHOR_RATIOS:
  • 0.25
  • 0.5
  • 0.75
  • 1
  • 1.25
  • 1.5
  • 1.75
    ANCHOR_SCALES:
  • 4
  • 8
  • 16
  • 32
  • 64
  • 128
  • 256
    NUM_ANCHORS: 49
    dataset:
    NUM_CLASSES: 10
    dataset: kitti
    dataset_path: "./data/kitti"
    image_set: train
    root_path: "./data"
    test_image_set: test
    proposal: rpn
    TRAIN:
    lr: 0.0001

  lr_step: '4.83'
  warmup: false
  warmup_lr: 0.005

  typically we will use 4000 warmup step for single GPU on VOC

  warmup_step: 100
  begin_epoch: 0
  end_epoch: 80
  model_prefix: 'rcnn_kitti'

  whether resume training

  RESUME: false

  whether flip image

  FLIP: false

  whether shuffle image

  SHUFFLE: true

  whether use OHEM

  ENABLE_OHEM: false
  ENABLE_FOCALLOSS: true

  size of images for each device, 2 for rcnn, 1 for rpn and e2e

  BATCH_IMAGES: 1

  e2e changes behavior of anchor loader and metric

  END2END: true

  group images with similar aspect ratio

  ASPECT_GROUPING: true

  R-CNN

  rcnn rois batch size

  BATCH_ROIS: 128
  BATCH_ROIS_OHEM: 128

  rcnn rois sampling params

  FG_FRACTION: 0.25
  FG_THRESH: 0.5
  BG_THRESH_HI: 0.5
  BG_THRESH_LO: 0.1

  rcnn bounding box regression params

  BBOX_REGRESSION_THRESH: 0.5
  BBOX_WEIGHTS:

  • 1.0
  • 1.0
  • 1.0
  • 1.0

  RPN anchor loader

  rpn anchors batch size

  RPN_BATCH_SIZE: 256

  rpn anchors sampling params

  RPN_FG_FRACTION: 0.5
  RPN_POSITIVE_OVERLAP: 0.7
  RPN_NEGATIVE_OVERLAP: 0.3
  RPN_CLOBBER_POSITIVES: false

  rpn bounding box regression params

  RPN_BBOX_WEIGHTS:

  • 1.0
  • 1.0
  • 1.0
  • 1.0
    RPN_POSITIVE_WEIGHT: -1.0

  used for end2end training

  RPN proposal

  CXX_PROPOSAL: false
  RPN_NMS_THRESH: 0.7
  RPN_PRE_NMS_TOP_N: 6000
  RPN_POST_NMS_TOP_N: 300
  RPN_MIN_SIZE: 0

  approximate bounding box regression

  BBOX_NORMALIZATION_PRECOMPUTED: true
  BBOX_MEANS:

  • 0.0
  • 0.0
  • 0.0
  • 0.0
    BBOX_STDS:
  • 0.1
  • 0.1
  • 0.2
  • 0.2
    TEST:

  use rpn to generate proposal

  HAS_RPN: true

  size of images for each device

  BATCH_IMAGES: 1

  RPN proposal

  CXX_PROPOSAL: false
  RPN_NMS_THRESH: 0.7
  RPN_PRE_NMS_TOP_N: 6000
  RPN_POST_NMS_TOP_N: 300
  RPN_MIN_SIZE: 0

  RPN generate proposal

  PROPOSAL_NMS_THRESH: 0.7
  PROPOSAL_PRE_NMS_TOP_N: 20000
  PROPOSAL_POST_NMS_TOP_N: 2000
  PROPOSAL_MIN_SIZE: 0

  RCNN nms

  NMS: 0.75
  test_epoch: 73

[zimenglan-sysu-512]

hi @unsky,

thanks for sharing your hyper-parameters.

[zimenglan-sysu-512]

hi @unsky,
can you share the kitti(10 cls) dataset?

[unsky]

@zimenglan-sysu-512 e。。http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d

[iFighting]

i have train the focal loss with doformable conv several times
i found that the mAP is lower than the origin

• lr = 0.0005，warmup_lr = 0.00005
• AP@0.5 = 0.8002，AP@0.7 = 0.6792
  this is the performance on voc2007 test with training data 2012+2007 trainval

[unsky]

@iFighting focal loss is a method to process the imbalance examples. it is not better in the balance scene. for the level of imbalance , you must choose a suit alpha and gamma

[iFighting]

@unsky actually, i have tried several different alpha and gamma