vit-base_rvl_cdip-N1K_AURC_4 – AI Model by bdpc | AlphaNeural AI

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bdpc/
vit-base_rvl_cdip-N1K_AURC_4

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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_AURC_4

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: 0.2768
Accuracy: 0.8738
Brier Loss: 0.2167
Nll: 0.9821
F1 Micro: 0.8738
F1 Macro: 0.8749
Ece: 0.0970
Aurc: 0.0292

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: 4
eval_batch_size: 4
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
0.1764	1.0	4000	0.3808	0.8217	0.2750	1.2675	0.8217	0.8194	0.1016	0.0461
0.1131	2.0	8000	0.3321	0.8413	0.2583	1.3120	0.8413	0.8421	0.0949	0.0418
0.113	3.0	12000	0.3781	0.8207	0.2910	1.4889	0.8207	0.8213	0.1162	0.0496
0.0814	4.0	16000	0.4793	0.8157	0.3036	1.4208	0.8157	0.8151	0.1302	0.0552
0.0542	5.0	20000	0.2914	0.8658	0.2279	1.1541	0.8658	0.8657	0.0955	0.0320
0.0238	6.0	24000	0.3059	0.8568	0.2401	1.1686	0.8568	0.8581	0.1012	0.0354
0.0197	7.0	28000	0.3077	0.8545	0.2390	1.1659	0.8545	0.8553	0.1059	0.0354
0.0116	8.0	32000	0.3169	0.8705	0.2172	1.0323	0.8705	0.8704	0.0918	0.0314
0.0054	9.0	36000	0.2850	0.8738	0.2199	1.0171	0.8738	0.8747	0.0960	0.0302
0.0128	10.0	40000	0.2768	0.8738	0.2167	0.9821	0.8738	0.8749	0.0970	0.0292

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3