vit-base_rvl_cdip-N1K_ce_4 – AI Model by bdpc | AlphaNeural AI

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bdpc/
vit-base_rvl_cdip-N1K_ce_4

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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_ce_4

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: 0.9480
Accuracy: 0.8792
Brier Loss: 0.2240
Nll: 1.0075
F1 Micro: 0.8793
F1 Macro: 0.8794
Ece: 0.1101
Aurc: 0.0274

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: 4
eval_batch_size: 4
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
0.4172	1.0	4000	0.6321	0.8475	0.2427	1.1862	0.8475	0.8484	0.0957	0.0352
0.3421	2.0	8000	0.6729	0.8645	0.2301	1.1766	0.8645	0.8642	0.1020	0.0295
0.2091	3.0	12000	0.7988	0.854	0.2563	1.1608	0.854	0.8555	0.1183	0.0352
0.1319	4.0	16000	0.8683	0.861	0.2503	1.1575	0.861	0.8617	0.1188	0.0354
0.0673	5.0	20000	0.9057	0.8642	0.2479	1.1524	0.8643	0.8635	0.1195	0.0314
0.0333	6.0	24000	0.9553	0.8605	0.2524	1.1006	0.8605	0.8600	0.1226	0.0366
0.0223	7.0	28000	0.9393	0.8708	0.2350	1.1027	0.8708	0.8713	0.1159	0.0274
0.0194	8.0	32000	1.0108	0.8705	0.2407	1.0850	0.8705	0.8704	0.1169	0.0309
0.0015	9.0	36000	0.9412	0.876	0.2291	1.0136	0.8760	0.8763	0.1123	0.0270
0.004	10.0	40000	0.9480	0.8792	0.2240	1.0075	0.8793	0.8794	0.1101	0.0274

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3