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
Add vitfs_128
Browse files- vitfs_128/aircraft_vitfs_micro_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/aircraft_vitfs_nano_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/aircraft_vitfs_tiny_patch16_gap_reg4_dinov2_bn_init_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/cars_vitfs_micro_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/cars_vitfs_nano_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/cars_vitfs_tiny_patch16_gap_reg4_dinov2_bn_init_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/cub_vitfs_micro_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth +3 -0
- vitfs_128/cub_vitfs_nano_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth +3 -0
vitfs_128/aircraft_vitfs_micro_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth
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size 10246135
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vitfs_128/aircraft_vitfs_nano_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth
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oid sha256:3d39cbe696a286d7c114f98c79d9ad78f79fd9275adee313651bfd1c60c921dc
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size 2712731
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vitfs_128/aircraft_vitfs_tiny_patch16_gap_reg4_dinov2_bn_init_resnet101_cal_tgda_109.pth
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oid sha256:4268681cb6908f3c10ed6cab3470a08453befc36d9fabfc48a55a46151cb4bd5
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size 22720679
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vitfs_128/cars_vitfs_micro_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth
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size 10284167
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vitfs_128/cars_vitfs_nano_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth
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vitfs_128/cars_vitfs_tiny_patch16_gap_reg4_dinov2_bn_init_resnet101_cal_tgda_109.pth
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vitfs_128/cub_vitfs_micro_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth
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oid sha256:b8622d824f0eabaef573cd775247582f4f6685160380616d68d37e15a16e6f99
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size 10285931
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vitfs_128/cub_vitfs_nano_patch16_gap_reg4_dinov2_bn_resnet101_cal_tgda_109.pth
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size 2737103
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