:sparkles: Add 2 CIFAR100 models

README.md

@@ -20,8 +20,8 @@ The code is based on [TorchUncertainty](https://github.com/ENSTA-U2IS-AI/torch-u
 This repository contains:
 - for classification on ImageNet with ViTs: 4 ViTs-S/16 trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
 - for classification on ImageNet with ResNets: 4 ResNet-50 trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
-- for classification on CIFAR-100: one CE-based and one LS-based (LS coefficient 0.2) DenseNet 
-- for segmentation: one CE-based and one LS-based (LS coefficient 0.2) DeepLabv3+ Resnet-101 
+- for classification on CIFAR-100: 4 DenseNet-BC trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
+- for segmentation: 4 DeepLabv3+ Resnet-101 trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
 - for nlp: one CE-based and one LS-based (LS coefficient 0.6) LSTM-MLP
 
 The rest of the models (notably on tabular data) used in the paper are trainable on CPU in the dedicated notebooks.

classification/cifar100/densenet_c100_ls01.safetensors

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classification/cifar100/densenet_c100_ls03.safetensors

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