vit-base_rvl_cdip-N1K_AURC_256 – AI Model by bdpc | AlphaNeural AI

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bdpc/
vit-base_rvl_cdip-N1K_AURC_256

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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_256

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: 0.2459
Accuracy: 0.8968
Brier Loss: 0.1720
Nll: 0.9246
F1 Micro: 0.8968
F1 Macro: 0.8967
Ece: 0.0709
Aurc: 0.0191

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: 256
eval_batch_size: 256
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
No log	1.0	63	0.1138	0.8922	0.1604	1.1695	0.8922	0.8926	0.0478	0.0170
No log	2.0	126	0.1565	0.8952	0.1607	1.1000	0.8952	0.8952	0.0532	0.0176
No log	3.0	189	0.1722	0.8972	0.1620	1.0250	0.8972	0.8973	0.0584	0.0175
No log	4.0	252	0.2006	0.897	0.1642	0.9921	0.897	0.8969	0.0615	0.0181
No log	5.0	315	0.2142	0.8988	0.1668	0.9670	0.8988	0.8986	0.0640	0.0183
No log	6.0	378	0.2207	0.8975	0.1688	0.9482	0.8975	0.8975	0.0674	0.0186
No log	7.0	441	0.2310	0.897	0.1700	0.9397	0.897	0.8969	0.0697	0.0188
0.008	8.0	504	0.2401	0.8968	0.1714	0.9268	0.8968	0.8966	0.0705	0.0190
0.008	9.0	567	0.2441	0.8975	0.1719	0.9262	0.8975	0.8974	0.0709	0.0191
0.008	10.0	630	0.2459	0.8968	0.1720	0.9246	0.8968	0.8967	0.0709	0.0191

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3