diff --git a/README.md b/README.md
index 3b59d85aeb7cb74382971abf343da684100cdd95..6c02f0efaebf36498a35270617ab00da1662744a 100644
--- a/README.md
+++ b/README.md
@@ -22,17 +22,17 @@ ending in the serial. Load them with [fgir-zoo](https://github.com/arkel23/fgir-
 
 | Folder | Serials | What | Files |
 |---|---|---|---|
-| `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 |
-| `lrnet_128` | 201-204 | Main LRNet table: low-resolution students at 128 px from a 448 px teacher | 24 |
-| `vitfs_448` | 100, 101 | Efficient ViTs and the ViTFS variants, teacher and student at 448 px | 54 |
-| `vitfs_224_t448` | 102 | Teacher at 448 px, student at 224 px | 15 |
-| `vitfs_224` | 103 | Teacher and student at 224 px | 9 |
-| `vitfs_128` | 109 | ViTFS tiny/micro/nano students at 128 px from a 448 px teacher | 8 |
-| `vit_ce_448` | 107 | The serial-101 models trained with cross-entropy only (no teacher) | 36 |
-| `vitfs_transfer` | 104-106 | A TGDA-trained student transferred to two new datasets with cross-entropy | 8 |
-| `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 |
-| `scratch_vs_pt_448` | 320, 321, 331 | 200 epochs from scratch with cross-entropy or KD, and ImageNet-pretrained with KD | 9 |
-| `sweeps_128` | 0, 1 | KD weight and temperature sweeps on CUB | 2 |
+| `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 |
+| `lrnet_128` | 50001-50004 | Main LRNet table: low-resolution students at 128 px from a 448 px teacher | 24 |
+| `vitfs_448` | 51001-51002, 51171-51176 | Efficient ViTs and the ViTFS variants, teacher and student at 448 px | 54 |
+| `vitfs_224_t448` | 51201, 51203 | Teacher at 448 px, student at 224 px | 15 |
+| `vitfs_224` | 51202 | Teacher and student at 224 px | 9 |
+| `vitfs_128` | 51401 | ViTFS tiny/micro/nano students at 128 px from a 448 px teacher | 8 |
+| `vit_ce_448` | 51081-51083 | The `vitfs_448` models trained with cross-entropy only (no teacher) | 36 |
+| `vitfs_transfer` | 51161-51163 | A TGDA-trained student transferred to two new datasets with cross-entropy | 8 |
+| `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 |
+| `scratch_vs_pt_448` | 50681-50682, 50691 | 200 epochs from scratch with cross-entropy or KD, and ImageNet-pretrained with KD | 9 |
+| `sweeps_128` | 50105, 50119 | KD weight and temperature sweeps on CUB | 2 |
 
 `manifest.csv` lists every file with its dataset, student, teacher, serial, seed, image size,
 class count, accuracy, SHA-256 and size.
@@ -45,7 +45,7 @@ from PIL import Image
 from torchvision import transforms
 from fgir_zoo import tgda
 
-model = tgda.create_model('sota_448/cub_resnet18_resnet101_cal_tgda_301')
+model = tgda.create_model('sota_448/cub_resnet18_resnet101_cal_tgda_50401')
 cfg = model.config
 tf = transforms.Compose([
     transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
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