vit-base_rvl-cdip_r2_32 – AI Model by jordyvl | AlphaNeural AI

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jordyvl/
vit-base_rvl-cdip_r2_32

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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vit-base_rvl-cdip_r2_32

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: 0.6372
Accuracy: 0.8985
Brier Loss: 0.1792
Nll: 1.1736
F1 Micro: 0.8985
F1 Macro: 0.8987
Ece: 0.0847
Aurc: 0.0201

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: 96
eval_batch_size: 96
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
0.1647	1.0	3334	0.4024	0.8887	0.1682	1.2086	0.8887	0.8891	0.0457	0.0178
0.1418	2.0	6668	0.4075	0.8941	0.1646	1.2066	0.8941	0.8942	0.0522	0.0177
0.0989	3.0	10002	0.4409	0.8932	0.1690	1.1966	0.8932	0.8932	0.0647	0.0175
0.0614	4.0	13336	0.4781	0.8944	0.1730	1.2083	0.8944	0.8951	0.0694	0.0181
0.0392	5.0	16670	0.5329	0.8959	0.1761	1.1777	0.8959	0.8958	0.0776	0.0187
0.0231	6.0	20004	0.5714	0.8957	0.1799	1.2083	0.8957	0.8958	0.0813	0.0198
0.0126	7.0	23338	0.6002	0.8966	0.1802	1.1732	0.8966	0.8972	0.0839	0.0197
0.0079	8.0	26672	0.6193	0.8984	0.1789	1.1849	0.8984	0.8985	0.0833	0.0200
0.0049	9.0	30006	0.6333	0.8976	0.1798	1.1906	0.8976	0.8978	0.0851	0.0205
0.0034	10.0	33340	0.6372	0.8985	0.1792	1.1736	0.8985	0.8987	0.0847	0.0201

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2