vit-base_rvl_cdip-N1K_aAURC_16 – AI Model by bdpc | AlphaNeural AI

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bdpc/
vit-base_rvl_cdip-N1K_aAURC_16

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image-classification

generated_from_trainer

jordyvl/vit-base_rvl-cdip

autotrain_compatible

endpoints_compatible

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vit-base_rvl_cdip-N1K_aAURC_16

This model is a fine-tuned version of jordyvl/vit-base_rvl-cdip on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5629
Accuracy: 0.8892
Brier Loss: 0.1995
Nll: 0.8643
F1 Micro: 0.8892
F1 Macro: 0.8898
Ece: 0.0923
Aurc: 0.0215

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
0.1794	1.0	1000	0.3827	0.8815	0.1829	1.1942	0.8815	0.8822	0.0573	0.0226
0.1415	2.0	2000	0.4705	0.8698	0.2118	1.1615	0.8698	0.8686	0.0859	0.0259
0.0725	3.0	3000	0.4582	0.8768	0.1996	1.0476	0.8768	0.8771	0.0845	0.0234
0.0388	4.0	4000	0.4958	0.879	0.2024	1.0000	0.879	0.8798	0.0877	0.0259
0.0153	5.0	5000	0.5171	0.8815	0.2047	0.9580	0.8815	0.8815	0.0942	0.0229
0.0069	6.0	6000	0.5334	0.8845	0.2021	0.9350	0.8845	0.8854	0.0922	0.0230
0.005	7.0	7000	0.5412	0.8905	0.1964	0.9179	0.8905	0.8907	0.0886	0.0218
0.0043	8.0	8000	0.5497	0.8892	0.1985	0.8970	0.8892	0.8900	0.0901	0.0225
0.0023	9.0	9000	0.5610	0.8878	0.1994	0.8679	0.8878	0.8883	0.0932	0.0220
0.0024	10.0	10000	0.5629	0.8892	0.1995	0.8643	0.8892	0.8898	0.0923	0.0215

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3