metafile.yml 1.8 KB

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  1. Collections:
  2. - Name: NAS-FPN
  3. Metadata:
  4. Training Data: COCO
  5. Training Techniques:
  6. - SGD with Momentum
  7. - Weight Decay
  8. Training Resources: 8x V100 GPUs
  9. Architecture:
  10. - NAS-FPN
  11. - ResNet
  12. Paper:
  13. URL: https://arxiv.org/abs/1904.07392
  14. Title: 'NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection'
  15. README: configs/nas_fpn/README.md
  16. Code:
  17. URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/nas_fpn.py#L67
  18. Version: v2.0.0
  19. Models:
  20. - Name: retinanet_r50_fpn_crop640-50e_coco
  21. In Collection: NAS-FPN
  22. Config: configs/nas_fpn/retinanet_r50_fpn_crop640-50e_coco.py
  23. Metadata:
  24. Training Memory (GB): 12.9
  25. inference time (ms/im):
  26. - value: 43.67
  27. hardware: V100
  28. backend: PyTorch
  29. batch size: 1
  30. mode: FP32
  31. resolution: (800, 1333)
  32. Epochs: 50
  33. Results:
  34. - Task: Object Detection
  35. Dataset: COCO
  36. Metrics:
  37. box AP: 37.9
  38. Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth
  39. - Name: retinanet_r50_nasfpn_crop640-50e_coco
  40. In Collection: NAS-FPN
  41. Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py
  42. Metadata:
  43. Training Memory (GB): 13.2
  44. inference time (ms/im):
  45. - value: 43.48
  46. hardware: V100
  47. backend: PyTorch
  48. batch size: 1
  49. mode: FP32
  50. resolution: (800, 1333)
  51. Epochs: 50
  52. Results:
  53. - Task: Object Detection
  54. Dataset: COCO
  55. Metrics:
  56. box AP: 40.5
  57. Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth