metafile.yml 1.5 KB

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  1. Collections:
  2. - Name: CAE
  3. Metadata:
  4. Training Data: ImageNet-1k
  5. Training Techniques:
  6. - AdamW
  7. Training Resources: 8x A100-80G GPUs
  8. Architecture:
  9. - ViT
  10. Paper:
  11. Title: Context Autoencoder for Self-Supervised Representation Learning
  12. URL: https://arxiv.org/abs/2202.03026
  13. README: configs/cae/README.md
  14. Models:
  15. - Name: cae_beit-base-p16_8xb256-amp-coslr-300e_in1k
  16. Metadata:
  17. Epochs: 300
  18. Batch Size: 2048
  19. FLOPs: 17581976064
  20. Parameters: 288429952
  21. Training Data: ImageNet-1k
  22. In Collection: CAE
  23. Results: null
  24. Weights: https://download.openmmlab.com/mmselfsup/1.x/cae/cae_vit-base-p16_8xb256-amp-coslr-300e_in1k/cae_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221230-808170f3.pth
  25. Config: configs/cae/cae_beit-base-p16_8xb256-amp-coslr-300e_in1k.py
  26. Downstream:
  27. - beit-base-p16_cae-pre_8xb128-coslr-100e_in1k
  28. - Name: beit-base-p16_cae-pre_8xb128-coslr-100e_in1k
  29. Metadata:
  30. Epochs: 100
  31. Batch Size: 1024
  32. FLOPs: 17581219584
  33. Parameters: 86682280
  34. Training Data: ImageNet-1k
  35. In Collection: CAE
  36. Results:
  37. - Task: Image Classification
  38. Dataset: ImageNet-1k
  39. Metrics:
  40. Top 1 Accuracy: 83.2
  41. Weights: https://download.openmmlab.com/mmselfsup/1.x/cae/cae_vit-base-p16_16xb128-fp16-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k_20220825-f3d234cd.pth
  42. Config: configs/cae/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py