metafile.yml
1.83 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Collections:
- Name: NAS-FPN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- NAS-FPN
- ResNet
Paper:
URL: https://arxiv.org/abs/1904.07392
Title: 'NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection'
README: configs/nas_fpn/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/nas_fpn.py#L67
Version: v2.0.0
Models:
- Name: retinanet_r50_fpn_crop640_50e_coco
In Collection: NAS-FPN
Config: configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
Metadata:
Training Memory (GB): 12.9
inference time (ms/im):
- value: 43.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 50
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 37.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth
- Name: retinanet_r50_nasfpn_crop640_50e_coco
In Collection: NAS-FPN
Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py
Metadata:
Training Memory (GB): 13.2
inference time (ms/im):
- value: 43.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 50
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth