vit-base_rvl_cdip_aurc

bdpc/vit-base_rvl_cdip_aurc

vit-base_rvl_cdip_aurc is a image classification model from bdpc. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Oct 20, 2023
Updated: Oct 20, 2023
SHA: 4ba6c041

Base models

jordyvl/vit-base_rvl-cdip

Model card

From the Hugging Face model README

vit-base_rvl_cdip_aurc

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: 0.2759
Accuracy: 0.893
Brier Loss: 0.1798
Nll: 0.8614
F1 Micro: 0.893
F1 Macro: 0.8928
Ece: 0.0750
Aurc: 0.0215

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
0.0303	1.0	500	0.1865	0.8795	0.1840	1.2087	0.8795	0.8791	0.0495	0.0241
0.0262	2.0	1000	0.2146	0.8788	0.1909	1.1956	0.8788	0.8789	0.0603	0.0257
0.0121	3.0	1500	0.2117	0.886	0.1799	1.0878	0.886	0.8865	0.0611	0.0230
0.0057	4.0	2000	0.2279	0.8878	0.1803	1.0108	0.8878	0.8879	0.0678	0.0228
0.0038	5.0	2500	0.2491	0.8872	0.1827	0.9661	0.8872	0.8877	0.0725	0.0234
0.0028	6.0	3000	0.2398	0.89	0.1806	0.9378	0.89	0.8901	0.0725	0.0215
0.0016	7.0	3500	0.2736	0.891	0.1792	0.8975	0.891	0.8914	0.0744	0.0221
0.0014	8.0	4000	0.2357	0.8905	0.1811	0.8993	0.8905	0.8910	0.0764	0.0210
0.001	9.0	4500	0.2714	0.8898	0.1807	0.8650	0.8898	0.8897	0.0783	0.0213
0.0009	10.0	5000	0.2759	0.893	0.1798	0.8614	0.893	0.8928	0.0750	0.0215

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Oct 20, 2023
Updated: Oct 20, 2023