vit-base_rvl_cdip-N1K_AURC_16 – AI Model by bdpc | AlphaNeural AI

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bdpc/
vit-base_rvl_cdip-N1K_AURC_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_AURC_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: 0.2895
Accuracy: 0.8925
Brier Loss: 0.1833
Nll: 0.8632
F1 Micro: 0.8925
F1 Macro: 0.8927
Ece: 0.0768
Aurc: 0.0218

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
0.0448	1.0	1000	0.1956	0.8758	0.1900	1.1701	0.8758	0.8769	0.0566	0.0252
0.0381	2.0	2000	0.2463	0.8715	0.1989	1.1688	0.8715	0.8716	0.0715	0.0261
0.0136	3.0	3000	0.2947	0.87	0.2081	1.0890	0.87	0.8693	0.0752	0.0271
0.0092	4.0	4000	0.2718	0.881	0.1901	1.0230	0.881	0.8811	0.0759	0.0253
0.0048	5.0	5000	0.2823	0.8812	0.1934	0.9914	0.8812	0.8814	0.0777	0.0238
0.0045	6.0	6000	0.2555	0.8855	0.1889	0.9305	0.8855	0.8861	0.0768	0.0223
0.0022	7.0	7000	0.2754	0.886	0.1873	0.8958	0.886	0.8860	0.0804	0.0221
0.0019	8.0	8000	0.2784	0.8858	0.1914	0.9248	0.8858	0.8866	0.0796	0.0229
0.0008	9.0	9000	0.2855	0.8878	0.1885	0.8671	0.8878	0.8876	0.0809	0.0226
0.0005	10.0	10000	0.2895	0.8925	0.1833	0.8632	0.8925	0.8927	0.0768	0.0218

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3