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_324
Browse files- students/serial_324/cub_lcnet_035_convnext_base.fb_in22k_ft_in1k_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_resnet101_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_resnetv2_101_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_resnetv2_101x3_bit.goog_in21k_ft_in1k_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_swin_base_patch4_window7_224_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_van_b3_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_vgg19_bn_cal_tgda_324.pth +3 -0
- students/serial_324/cub_lcnet_035_vit_b16_cal_tgda_324.pth +3 -0
students/serial_324/cub_lcnet_035_convnext_base.fb_in22k_ft_in1k_cal_tgda_324.pth
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size 2573445
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students/serial_324/cub_lcnet_035_resnet101_cal_tgda_324.pth
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students/serial_324/cub_lcnet_035_resnetv2_101_cal_tgda_324.pth
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students/serial_324/cub_lcnet_035_resnetv2_101x3_bit.goog_in21k_ft_in1k_cal_tgda_324.pth
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students/serial_324/cub_lcnet_035_swin_base_patch4_window7_224_cal_tgda_324.pth
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oid sha256:6c7327533709c2fd3c11a83e23cd8c05239142925922d202df136ef89998d988
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students/serial_324/cub_lcnet_035_van_b3_cal_tgda_324.pth
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students/serial_324/cub_lcnet_035_vgg19_bn_cal_tgda_324.pth
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students/serial_324/cub_lcnet_035_vit_b16_cal_tgda_324.pth
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