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Remove the old serial names; manifest and card for the new TGDA serials

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  1. README.md +12 -12
  2. ablations_128/aircraft_lrnet14_248.pth +0 -3
  3. ablations_128/aircraft_lrnet14_262.pth +0 -3
  4. ablations_128/aircraft_lrnet14_263.pth +0 -3
  5. ablations_128/aircraft_lrnet14_cal_247.pth +0 -3
  6. ablations_128/aircraft_lrnet14_resnet101_cal_245.pth +0 -3
  7. ablations_128/aircraft_lrnet14_resnet101_cal_246.pth +0 -3
  8. ablations_128/aircraft_lrnet14_resnet101_cal_tgda_241.pth +0 -3
  9. ablations_128/aircraft_lrnet14_resnet101_cal_tgda_243.pth +0 -3
  10. ablations_128/aircraft_lrnet14_resnet101_cal_tgda_244.pth +0 -3
  11. ablations_128/aircraft_lrnet14_resnet101_cal_tgda_251.pth +0 -3
  12. ablations_128/aircraft_resnet101d_resnet101_cal_tgda_214.pth +0 -3
  13. ablations_128/aircraft_resnet18_278.pth +0 -3
  14. ablations_128/aircraft_resnet18_cal_277.pth +0 -3
  15. ablations_128/aircraft_resnet18_resnet101_cal_275.pth +0 -3
  16. ablations_128/aircraft_resnet18_resnet101_cal_276.pth +0 -3
  17. ablations_128/aircraft_resnet18_resnet101_cal_tgda_221.pth +0 -3
  18. ablations_128/aircraft_resnet18_resnet101_cal_tgda_222.pth +0 -3
  19. ablations_128/aircraft_resnet18_resnet101_cal_tgda_223.pth +0 -3
  20. ablations_128/aircraft_resnet18_resnet101_cal_tgda_271.pth +0 -3
  21. ablations_128/aircraft_resnet18_resnet101_cal_tgda_272.pth +0 -3
  22. ablations_128/aircraft_resnet18_resnet101_cal_tgda_273.pth +0 -3
  23. ablations_128/aircraft_resnet18_resnet101_cal_tgda_274.pth +0 -3
  24. ablations_128/aircraft_resnet18_resnet101_cal_tgda_281.pth +0 -3
  25. ablations_128/aircraft_resnet34d_resnet101_cal_tgda_212.pth +0 -3
  26. ablations_128/cars_lrnet14_248.pth +0 -3
  27. ablations_128/cars_lrnet14_261.pth +0 -3
  28. ablations_128/cars_lrnet14_263.pth +0 -3
  29. ablations_128/cars_lrnet14_cal_247.pth +0 -3
  30. ablations_128/cars_lrnet14_resnet101_cal_245.pth +0 -3
  31. ablations_128/cars_lrnet14_resnet101_cal_246.pth +0 -3
  32. ablations_128/cars_lrnet14_resnet101_cal_tgda_241.pth +0 -3
  33. ablations_128/cars_lrnet14_resnet101_cal_tgda_242.pth +0 -3
  34. ablations_128/cars_lrnet14_resnet101_cal_tgda_243.pth +0 -3
  35. ablations_128/cars_lrnet14_resnet101_cal_tgda_244.pth +0 -3
  36. ablations_128/cars_lrnet14_resnet101_cal_tgda_251.pth +0 -3
  37. ablations_128/cars_resnet101d_resnet101_cal_tgda_214.pth +0 -3
  38. ablations_128/cars_resnet18_278.pth +0 -3
  39. ablations_128/cars_resnet18_cal_277.pth +0 -3
  40. ablations_128/cars_resnet18_resnet101_cal_275.pth +0 -3
  41. ablations_128/cars_resnet18_resnet101_cal_276.pth +0 -3
  42. ablations_128/cars_resnet18_resnet101_cal_tgda_221.pth +0 -3
  43. ablations_128/cars_resnet18_resnet101_cal_tgda_222.pth +0 -3
  44. ablations_128/cars_resnet18_resnet101_cal_tgda_223.pth +0 -3
  45. ablations_128/cars_resnet18_resnet101_cal_tgda_271.pth +0 -3
  46. ablations_128/cars_resnet18_resnet101_cal_tgda_272.pth +0 -3
  47. ablations_128/cars_resnet18_resnet101_cal_tgda_273.pth +0 -3
  48. ablations_128/cars_resnet18_resnet101_cal_tgda_274.pth +0 -3
  49. ablations_128/cars_resnet18_resnet101_cal_tgda_281.pth +0 -3
  50. ablations_128/cars_resnet34d_resnet101_cal_tgda_212.pth +0 -3
README.md CHANGED
@@ -22,17 +22,17 @@ ending in the serial. Load them with [fgir-zoo](https://github.com/arkel23/fgir-
22
 
23
  | Folder | Serials | What | Files |
24
  |---|---|---|---|
25
- | `sota_448` | 301-304 | Main state-of-the-art table: ResNet-18/34/50/101 students, TGDA with TrivialAugment, label smoothing and stochastic depth | 12 |
26
- | `lrnet_128` | 201-204 | Main LRNet table: low-resolution students at 128 px from a 448 px teacher | 24 |
27
- | `vitfs_448` | 100, 101 | Efficient ViTs and the ViTFS variants, teacher and student at 448 px | 54 |
28
- | `vitfs_224_t448` | 102 | Teacher at 448 px, student at 224 px | 15 |
29
- | `vitfs_224` | 103 | Teacher and student at 224 px | 9 |
30
- | `vitfs_128` | 109 | ViTFS tiny/micro/nano students at 128 px from a 448 px teacher | 8 |
31
- | `vit_ce_448` | 107 | The serial-101 models trained with cross-entropy only (no teacher) | 36 |
32
- | `vitfs_transfer` | 104-106 | A TGDA-trained student transferred to two new datasets with cross-entropy | 8 |
33
- | `ablations_128` | 212, 214, 221-223, 241-248, 251, 261-263, 271-278, 281 | Ablations: TGDA without extra regularization, one- vs two-stage training, augmentation type, teacher type, transfer | 73 |
34
- | `scratch_vs_pt_448` | 320, 321, 331 | 200 epochs from scratch with cross-entropy or KD, and ImageNet-pretrained with KD | 9 |
35
- | `sweeps_128` | 0, 1 | KD weight and temperature sweeps on CUB | 2 |
36
 
37
  `manifest.csv` lists every file with its dataset, student, teacher, serial, seed, image size,
38
  class count, accuracy, SHA-256 and size.
@@ -45,7 +45,7 @@ from PIL import Image
45
  from torchvision import transforms
46
  from fgir_zoo import tgda
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48
- model = tgda.create_model('sota_448/cub_resnet18_resnet101_cal_tgda_301')
49
  cfg = model.config
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  tf = transforms.Compose([
51
  transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
 
22
 
23
  | Folder | Serials | What | Files |
24
  |---|---|---|---|
25
+ | `sota_448` | 50401-50404 | Main state-of-the-art table: ResNet-18/34/50/101 students, TGDA with TrivialAugment, label smoothing and stochastic depth | 12 |
26
+ | `lrnet_128` | 50001-50004 | Main LRNet table: low-resolution students at 128 px from a 448 px teacher | 24 |
27
+ | `vitfs_448` | 51001-51002, 51171-51176 | Efficient ViTs and the ViTFS variants, teacher and student at 448 px | 54 |
28
+ | `vitfs_224_t448` | 51201, 51203 | Teacher at 448 px, student at 224 px | 15 |
29
+ | `vitfs_224` | 51202 | Teacher and student at 224 px | 9 |
30
+ | `vitfs_128` | 51401 | ViTFS tiny/micro/nano students at 128 px from a 448 px teacher | 8 |
31
+ | `vit_ce_448` | 51081-51083 | The `vitfs_448` models trained with cross-entropy only (no teacher) | 36 |
32
+ | `vitfs_transfer` | 51161-51163 | A TGDA-trained student transferred to two new datasets with cross-entropy | 8 |
33
+ | `ablations_128` | 50041-50043, 50071-50075, 50081-50084, 50161-50163, 50202, 50204, 50271-50275, 50281-50284 | Ablations: TGDA without extra regularization, one- vs two-stage training, augmentation type, teacher type, transfer | 73 |
34
+ | `scratch_vs_pt_448` | 50681-50682, 50691 | 200 epochs from scratch with cross-entropy or KD, and ImageNet-pretrained with KD | 9 |
35
+ | `sweeps_128` | 50105, 50119 | KD weight and temperature sweeps on CUB | 2 |
36
 
37
  `manifest.csv` lists every file with its dataset, student, teacher, serial, seed, image size,
38
  class count, accuracy, SHA-256 and size.
 
45
  from torchvision import transforms
46
  from fgir_zoo import tgda
47
 
48
+ model = tgda.create_model('sota_448/cub_resnet18_resnet101_cal_tgda_50401')
49
  cfg = model.config
50
  tf = transforms.Compose([
51
  transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
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