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- Collections:
- - Name: RetinaNet
- Metadata:
- Training Data: COCO
- Training Techniques:
- - SGD with Momentum
- - Weight Decay
- Training Resources: 8x V100 GPUs
- Architecture:
- - Focal Loss
- - FPN
- - ResNet
- Paper:
- URL: https://arxiv.org/abs/1708.02002
- Title: 'Focal Loss for Dense Object Detection'
- README: configs/retinanet/README.md
- Code:
- URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/retinanet.py#L6
- Version: v2.0.0
-
- Models:
- - Name: retinanet_r50_caffe_fpn_1x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 3.5
- inference time (ms/im):
- - value: 53.76
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 36.3
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth
-
- - Name: retinanet_r50_fpn_1x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r50_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 3.8
- 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: 36.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth
-
- - Name: retinanet_r50_fpn_2x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r50_fpn_2x_coco.py
- Metadata:
- Epochs: 24
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 37.4
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth
-
- - Name: retinanet_r50_fpn_mstrain_3x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py
- Metadata:
- Epochs: 36
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 39.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth
-
- - Name: retinanet_r101_caffe_fpn_1x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 5.5
- inference time (ms/im):
- - value: 68.03
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 38.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth
-
- - Name: retinanet_r101_caffe_fpn_mstrain_3x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
- Metadata:
- Epochs: 36
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.7
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth
-
- - Name: retinanet_r101_fpn_1x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r101_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 5.7
- inference time (ms/im):
- - value: 66.67
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 38.5
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth
-
- - Name: retinanet_r101_fpn_2x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py
- Metadata:
- Training Memory (GB): 5.7
- inference time (ms/im):
- - value: 66.67
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 24
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 38.9
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth
-
- - Name: retinanet_r101_fpn_mstrain_3x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py
- Metadata:
- Epochs: 36
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth
-
-
- - Name: retinanet_x101_32x4d_fpn_1x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 7.0
- inference time (ms/im):
- - value: 82.64
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 39.9
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth
-
- - Name: retinanet_x101_32x4d_fpn_2x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
- Metadata:
- Training Memory (GB): 7.0
- inference time (ms/im):
- - value: 82.64
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 24
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.1
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth
-
- - Name: retinanet_x101_64x4d_fpn_1x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py
- Metadata:
- Training Memory (GB): 10.0
- inference time (ms/im):
- - value: 114.94
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 12
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41.0
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth
-
- - Name: retinanet_x101_64x4d_fpn_2x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py
- Metadata:
- Training Memory (GB): 10.0
- inference time (ms/im):
- - value: 114.94
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (800, 1333)
- Epochs: 24
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 40.8
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth
-
- - Name: retinanet_x101_64x4d_fpn_mstrain_3x_coco
- In Collection: RetinaNet
- Config: configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
- Metadata:
- Epochs: 36
- Results:
- - Task: Object Detection
- Dataset: COCO
- Metrics:
- box AP: 41.6
- Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth
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