metafile.yml 1.6 KB

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  1. Collections:
  2. - Name: SwAV
  3. Metadata:
  4. Training Data: ImageNet-1k
  5. Training Techniques:
  6. - LARS
  7. Training Resources: 8x V100 GPUs
  8. Architecture:
  9. - ResNet
  10. - SwAV
  11. Paper:
  12. Title: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
  13. URL: https://arxiv.org/abs/2006.09882
  14. README: configs/swav/README.md
  15. Models:
  16. - Name: swav_resnet50_8xb32-mcrop-coslr-200e_in1k-224px-96px
  17. Metadata:
  18. Epochs: 200
  19. Batch Size: 256
  20. FLOPs: 4109364224
  21. Parameters: 28354752
  22. Training Data: ImageNet-1k
  23. In Collection: SwAV
  24. Results: null
  25. Weights: https://download.openmmlab.com/mmselfsup/1.x/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96_20220825-5b3fc7fc.pth
  26. Config: configs/swav/swav_resnet50_8xb32-mcrop-coslr-200e_in1k-224px-96px.py
  27. Downstream:
  28. - resnet50_swav-pre_8xb32-linear-coslr-100e_in1k
  29. - Name: resnet50_swav-pre_8xb32-linear-coslr-100e_in1k
  30. Metadata:
  31. Epochs: 100
  32. Batch Size: 256
  33. FLOPs: 4109464576
  34. Parameters: 25557032
  35. Training Data: ImageNet-1k
  36. In Collection: SwAV
  37. Results:
  38. - Task: Image Classification
  39. Dataset: ImageNet-1k
  40. Metrics:
  41. Top 1 Accuracy: 70.5
  42. Weights: https://download.openmmlab.com/mmselfsup/1.x/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96/resnet50_linear-8xb32-coslr-100e_in1k/resnet50_linear-8xb32-coslr-100e_in1k_20220825-80341e08.pth
  43. Config: configs/swav/benchmarks/resnet50_8xb512-linear-coslr-90e_in1k.py