Image Classification
timm
PyTorch
vision-transformer
knowledge-distillation
feature-distillation
model-compression
interpretability
explainability
representation-analysis
encoding-mismatch
spectral-energy-pattern
spectral-kd
low-rank
pca
svd
deit-tiny
distilled-deit-tiny
cait-s24
lift
widelast
icml-2026
Eval Results (legacy)
Instructions to use Huiyuancs/Encoding_Mismatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Huiyuancs/Encoding_Mismatch with timm:
import timm model = timm.create_model("hf_hub:Huiyuancs/Encoding_Mismatch", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0009e4859179704c4df7fb3c85a73e323da911c9d136ec37df044debf854d11
- Size of remote file:
- 117 MB
- SHA256:
- 665d28fe07ee8a107d737c8a026f540dd050e24ce0e1d3e1ea4e971106d734f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.