Log In Sign Up
Models:
David Feaster
DavidFeaster/
Surgical-Bleeding-AMAGI
0
Downloads: 1
[6d389a]: / configs / recognition / tin / metafile.yml

Download this file

77 lines (76 with data), 3.0 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
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
Collections:

- Name: TIN

  README: configs/recognition/tin/README.md

  Paper:

    URL: https://arxiv.org/abs/2001.06499

    Title: Temporal Interlacing Network

Models:

- Config: configs/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb.py

  In Collection: TIN

  Metadata:

    Architecture: ResNet50

    Batch Size: 6

    Epochs: 40

    FLOPs: 32962097536

    Parameters: 23895566

    Pretrained: ImageNet

    Resolution: height 100

    Training Data: SthV1

    Training Resources: 32 GPUs

  Modality: RGB

  Name: tin_r50_1x1x8_40e_sthv1_rgb

  Results:

  - Dataset: SthV1

    Metrics:

      Top 1 Accuracy: 44.25

      Top 5 Accuracy: 73.94

    Task: Action Recognition

  Training Json Log: https://download.openmmlab.com/mmaction/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb/20200729_034132.log.json

  Training Log: https://download.openmmlab.com/mmaction/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb/20200729_034132.log

  Weights: https://download.openmmlab.com/mmaction/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb/tin_r50_1x1x8_40e_sthv1_rgb_20200729-4a33db86.pth

- Config: configs/recognition/tin/tin_r50_1x1x8_40e_sthv2_rgb.py

  In Collection: TIN

  Metadata:

    Architecture: ResNet50

    Batch Size: 6

    Epochs: 40

    FLOPs: 32962097536

    Parameters: 23895566

    Pretrained: ImageNet

    Resolution: height 240

    Training Data: SthV2

    Training Resources: 32 GPUs

  Modality: RGB

  Name: tin_r50_1x1x8_40e_sthv2_rgb

  Results:

  - Dataset: SthV2

    Metrics:

      Top 1 Accuracy: 56.7

      Top 5 Accuracy: 83.62

    Task: Action Recognition

  Training Json Log: https://download.openmmlab.com/mmaction/recognition/tin/tin_r50_1x1x8_40e_sthv2_rgb/20200912_225451.log.json

  Training Log: https://download.openmmlab.com/mmaction/recognition/tin/tin_r50_1x1x8_40e_sthv2_rgb/20200912_225451.log

  Weights: https://download.openmmlab.com/mmaction/recognition/tin/tin_r50_1x1x8_40e_sthv2_rgb/tin_r50_1x1x8_40e_sthv2_rgb_20200912-b27a7337.pth

- Config: configs/recognition/tin/tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb.py

  In Collection: TIN

  Metadata:

    Architecture: ResNet50

    Batch Size: 6

    Epochs: 50

    FLOPs: 32965800320

    Parameters: 24358640

    Pretrained: TSM-Kinetics400

    Resolution: short-side 256

    Training Data: Kinetics-400

    Training Resources: 32 GPUs

  Modality: RGB

  Name: tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb

  Results:

  - Dataset: Kinetics-400

    Metrics:

      Top 1 Accuracy: 70.89

      Top 5 Accuracy: 89.89

    Task: Action Recognition

  Training Json Log: https://download.openmmlab.com/mmaction/recognition/tin/tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb/20200809_142447.log.json

  Training Log: https://download.openmmlab.com/mmaction/recognition/tin/tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb/20200809_142447.log

  Weights: https://download.openmmlab.com/mmaction/recognition/tin/tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb/tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb_20200810-4a146a70.pth