vit-base_rvl_cdip-N1K_AURC_128 – AI Model by bdpc | AlphaNeural AI

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bdpc/
vit-base_rvl_cdip-N1K_AURC_128

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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_128

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: 0.2754
Accuracy: 0.8962
Brier Loss: 0.1742
Nll: 0.8794
F1 Micro: 0.8962
F1 Macro: 0.8963
Ece: 0.0736
Aurc: 0.0200

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: 128
eval_batch_size: 128
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	125	0.1357	0.8898	0.1657	1.2064	0.8898	0.8907	0.0492	0.0181
No log	2.0	250	0.1615	0.898	0.1602	1.0955	0.898	0.8986	0.0473	0.0181
No log	3.0	375	0.1795	0.896	0.1630	1.0031	0.8960	0.8959	0.0599	0.0180
0.0132	4.0	500	0.2094	0.8978	0.1662	0.9561	0.8978	0.8977	0.0633	0.0187
0.0132	5.0	625	0.2290	0.898	0.1692	0.9249	0.898	0.8979	0.0665	0.0190
0.0132	6.0	750	0.2430	0.898	0.1714	0.9150	0.898	0.8981	0.0690	0.0194
0.0132	7.0	875	0.2567	0.898	0.1718	0.8888	0.898	0.8979	0.0702	0.0196
0.0022	8.0	1000	0.2740	0.8975	0.1734	0.8800	0.8975	0.8975	0.0718	0.0199
0.0022	9.0	1125	0.2715	0.896	0.1743	0.8824	0.8960	0.8960	0.0737	0.0199
0.0022	10.0	1250	0.2754	0.8962	0.1742	0.8794	0.8962	0.8963	0.0736	0.0200

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3