vit-base_rvl_cdip-N1K_AURC_32

bdpc/vit-base_rvl_cdip-N1K_AURC_32

vit-base_rvl_cdip-N1K_AURC_32 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: a88afb9c

Base models

jordyvl/vit-base_rvl-cdip

Model card

From the Hugging Face model README

vit-base_rvl_cdip-N1K_AURC_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: 0.3439
Accuracy: 0.8962
Brier Loss: 0.1805
Nll: 0.8184
F1 Micro: 0.8962
F1 Macro: 0.8963
Ece: 0.0767
Aurc: 0.0220

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: 32
eval_batch_size: 32
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
0.0301	1.0	500	0.1897	0.8808	0.1804	1.1636	0.8808	0.8807	0.0528	0.0227
0.0229	2.0	1000	0.2504	0.883	0.1834	1.1357	0.883	0.8832	0.0573	0.0248
0.0081	3.0	1500	0.2251	0.8858	0.1787	1.0242	0.8858	0.8858	0.0653	0.0221
0.004	4.0	2000	0.3075	0.886	0.1831	0.9279	0.886	0.8850	0.0744	0.0227
0.0023	5.0	2500	0.2491	0.8908	0.1791	0.9302	0.8907	0.8916	0.0728	0.0212
0.0014	6.0	3000	0.3067	0.8925	0.1795	0.8631	0.8925	0.8929	0.0752	0.0215
0.0012	7.0	3500	0.3277	0.8925	0.1812	0.8729	0.8925	0.8922	0.0764	0.0218
0.0009	8.0	4000	0.3386	0.895	0.1797	0.8406	0.895	0.8951	0.0760	0.0219
0.0007	9.0	4500	0.3383	0.8968	0.1808	0.8293	0.8968	0.8969	0.0747	0.0220
0.0006	10.0	5000	0.3439	0.8962	0.1805	0.8184	0.8962	0.8963	0.0767	0.0220

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