Instructions to use ERISLab/TGDA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ERISLab/TGDA with timm:
import timm model = timm.create_model("hf-hub:ERISLab/TGDA", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Remove the old serial names; manifest and card for the new TGDA serials
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +12 -12
- ablations_128/aircraft_lrnet14_248.pth +0 -3
- ablations_128/aircraft_lrnet14_262.pth +0 -3
- ablations_128/aircraft_lrnet14_263.pth +0 -3
- ablations_128/aircraft_lrnet14_cal_247.pth +0 -3
- ablations_128/aircraft_lrnet14_resnet101_cal_245.pth +0 -3
- ablations_128/aircraft_lrnet14_resnet101_cal_246.pth +0 -3
- ablations_128/aircraft_lrnet14_resnet101_cal_tgda_241.pth +0 -3
- ablations_128/aircraft_lrnet14_resnet101_cal_tgda_243.pth +0 -3
- ablations_128/aircraft_lrnet14_resnet101_cal_tgda_244.pth +0 -3
- ablations_128/aircraft_lrnet14_resnet101_cal_tgda_251.pth +0 -3
- ablations_128/aircraft_resnet101d_resnet101_cal_tgda_214.pth +0 -3
- ablations_128/aircraft_resnet18_278.pth +0 -3
- ablations_128/aircraft_resnet18_cal_277.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_275.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_276.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_221.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_222.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_223.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_271.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_272.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_273.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_274.pth +0 -3
- ablations_128/aircraft_resnet18_resnet101_cal_tgda_281.pth +0 -3
- ablations_128/aircraft_resnet34d_resnet101_cal_tgda_212.pth +0 -3
- ablations_128/cars_lrnet14_248.pth +0 -3
- ablations_128/cars_lrnet14_261.pth +0 -3
- ablations_128/cars_lrnet14_263.pth +0 -3
- ablations_128/cars_lrnet14_cal_247.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_245.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_246.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_tgda_241.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_tgda_242.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_tgda_243.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_tgda_244.pth +0 -3
- ablations_128/cars_lrnet14_resnet101_cal_tgda_251.pth +0 -3
- ablations_128/cars_resnet101d_resnet101_cal_tgda_214.pth +0 -3
- ablations_128/cars_resnet18_278.pth +0 -3
- ablations_128/cars_resnet18_cal_277.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_275.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_276.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_221.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_222.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_223.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_271.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_272.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_273.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_274.pth +0 -3
- ablations_128/cars_resnet18_resnet101_cal_tgda_281.pth +0 -3
- ablations_128/cars_resnet34d_resnet101_cal_tgda_212.pth +0 -3
README.md
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| Folder | Serials | What | Files |
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| `sota_448` |
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| `lrnet_128` |
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`manifest.csv` lists every file with its dataset, student, teacher, serial, seed, image size,
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class count, accuracy, SHA-256 and size.
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from torchvision import transforms
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from fgir_zoo import tgda
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model = tgda.create_model('sota_448/
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cfg = model.config
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tf = transforms.Compose([
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transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
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| Folder | Serials | What | Files |
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| `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 |
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| `lrnet_128` | 50001-50004 | Main LRNet table: low-resolution students at 128 px from a 448 px teacher | 24 |
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| `vitfs_448` | 51001-51002, 51171-51176 | Efficient ViTs and the ViTFS variants, teacher and student at 448 px | 54 |
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| `vitfs_224_t448` | 51201, 51203 | Teacher at 448 px, student at 224 px | 15 |
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| `vitfs_224` | 51202 | Teacher and student at 224 px | 9 |
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| `vitfs_128` | 51401 | ViTFS tiny/micro/nano students at 128 px from a 448 px teacher | 8 |
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| `vit_ce_448` | 51081-51083 | The `vitfs_448` models trained with cross-entropy only (no teacher) | 36 |
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| `vitfs_transfer` | 51161-51163 | A TGDA-trained student transferred to two new datasets with cross-entropy | 8 |
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| `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 |
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| `scratch_vs_pt_448` | 50681-50682, 50691 | 200 epochs from scratch with cross-entropy or KD, and ImageNet-pretrained with KD | 9 |
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| `sweeps_128` | 50105, 50119 | KD weight and temperature sweeps on CUB | 2 |
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`manifest.csv` lists every file with its dataset, student, teacher, serial, seed, image size,
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class count, accuracy, SHA-256 and size.
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from torchvision import transforms
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from fgir_zoo import tgda
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model = tgda.create_model('sota_448/cub_resnet18_resnet101_cal_tgda_50401')
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cfg = model.config
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tf = transforms.Compose([
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transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
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