Instructions to use ERISLab/FGIR-KD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ERISLab/FGIR-KD with timm:
import timm model = timm.create_model("hf-hub:ERISLab/FGIR-KD", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Add students/serial_322
Browse files- students/serial_322/cub_lcnet_035_convnext_base.fb_in22k_ft_in1k_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_resnet101_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_resnetv2_101_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_resnetv2_101x3_bit.goog_in21k_ft_in1k_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_swin_base_patch4_window7_224_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_van_b3_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_vgg19_bn_cal_tgda_322.pth +3 -0
- students/serial_322/cub_lcnet_035_vit_b16_cal_tgda_322.pth +3 -0
students/serial_322/cub_lcnet_035_convnext_base.fb_in22k_ft_in1k_cal_tgda_322.pth
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size 2573445
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students/serial_322/cub_lcnet_035_resnet101_cal_tgda_322.pth
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oid sha256:96c7d6049df0e524adcb458e6ffb702511fd31b718cc6d47983d9b481df268b7
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size 2564181
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students/serial_322/cub_lcnet_035_resnetv2_101_cal_tgda_322.pth
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students/serial_322/cub_lcnet_035_resnetv2_101x3_bit.goog_in21k_ft_in1k_cal_tgda_322.pth
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students/serial_322/cub_lcnet_035_swin_base_patch4_window7_224_cal_tgda_322.pth
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oid sha256:169bb9d351f3bbcc98b9f92d7067c986ebfda686e02fedf10eab35026cfc5377
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size 2573093
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students/serial_322/cub_lcnet_035_van_b3_cal_tgda_322.pth
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students/serial_322/cub_lcnet_035_vgg19_bn_cal_tgda_322.pth
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oid sha256:7e4b1986ca6117d8a8a160e1c513b6e02a37378dcd87875a8099ea912c191427
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size 2564005
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students/serial_322/cub_lcnet_035_vit_b16_cal_tgda_322.pth
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oid sha256:e77ca3ac13b362e25584a38c992df376519098c02040bc30f92debfba0cda0d3
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size 2563829
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