vit-small_rvl_cdip – AI Model by jordyvl | AlphaNeural AI

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jordyvl/
vit-small_rvl_cdip

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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vit-small_rvl_cdip

This model is a fine-tuned version of WinKawaks/vit-small-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0854
Accuracy: 0.9110
Brier Loss: 0.1305
Nll: 1.2339
F1 Micro: 0.9110
F1 Macro: 0.9112
Ece: 0.0097
Aurc: 0.0122

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: 0.0001
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
0.2638	1.0	2500	0.2093	0.8574	0.2035	1.6580	0.8574	0.8564	0.0105	0.0272
0.1559	2.0	5000	0.1413	0.8854	0.1645	1.5062	0.8854	0.8863	0.0101	0.0185
0.1036	3.0	7500	0.1212	0.8994	0.1461	1.4353	0.8994	0.8999	0.0096	0.0150
0.0693	4.0	10000	0.1196	0.9013	0.1440	1.3827	0.9013	0.9017	0.0152	0.0148
0.0451	5.0	12500	0.1094	0.9062	0.1391	1.3099	0.9062	0.9064	0.0124	0.0139
0.0317	6.0	15000	0.0997	0.9073	0.1357	1.2889	0.9073	0.9078	0.0091	0.0132
0.0224	7.0	17500	0.0961	0.9081	0.1348	1.2705	0.9081	0.9082	0.0100	0.0129
0.0166	8.0	20000	0.0890	0.9099	0.1328	1.2484	0.9099	0.9102	0.0085	0.0126
0.0117	9.0	22500	0.0862	0.9096	0.1316	1.2428	0.9096	0.9100	0.0100	0.0123
0.0085	10.0	25000	0.0854	0.9110	0.1305	1.2339	0.9110	0.9112	0.0097	0.0122

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2