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metafile.yml
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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: https://arxiv.org/abs/1708.02002
README: configs/retinanet/README.md
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_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_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_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