Collections:
  - Name: Libra R-CNN
    Metadata:
      Training Data: COCO
      Training Techniques:
        - IoU-Balanced Sampling
        - SGD with Momentum
        - Weight Decay
      Training Resources: 8x V100 GPUs
      Architecture:
        - Balanced Feature Pyramid
    Paper:
      URL: https://arxiv.org/abs/1904.02701
      Title: 'Libra R-CNN: Towards Balanced Learning for Object Detection'
    README: configs/libra_rcnn/README.md
    Code:
      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/bfp.py#L10
      Version: v2.0.0

Models:
  - Name: libra-faster-rcnn_r50_fpn_1x_coco
    In Collection: Libra R-CNN
    Config: configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py
    Metadata:
      Training Memory (GB): 4.6
      inference time (ms/im):
        - value: 52.63
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
          resolution: (800, 1333)
      Epochs: 12
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 38.3
    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth

  - Name: libra-faster-rcnn_r101_fpn_1x_coco
    In Collection: Libra R-CNN
    Config: configs/libra_rcnn/libra-card_faster-rcnn_r101_fpn_1x_coco.py
    Metadata:
      Training Memory (GB): 6.5
      inference time (ms/im):
        - value: 69.44
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
          resolution: (800, 1333)
      Epochs: 12
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 40.1
    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth

  - Name: libra-faster-rcnn_x101-64x4d_fpn_1x_coco
    In Collection: Libra R-CNN
    Config: configs/libra_rcnn/libra-faster-rcnn_x101-64x4d_fpn_1x_coco.py
    Metadata:
      Training Memory (GB): 10.8
      inference time (ms/im):
        - value: 117.65
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
          resolution: (800, 1333)
      Epochs: 12
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 42.7
    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth

  - Name: libra-retinanet_r50_fpn_1x_coco
    In Collection: Libra R-CNN
    Config: configs/libra_rcnn/libra-retinanet_r50_fpn_1x_coco.py
    Metadata:
      Training Memory (GB): 4.2
      inference time (ms/im):
        - value: 56.5
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
          resolution: (800, 1333)
      Epochs: 12
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 37.6
    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth
