vit-base_rvl_cdip-N1K_AURC_64

bdpc/vit-base_rvl_cdip-N1K_AURC_64

vit-base_rvl_cdip-N1K_AURC_64 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: 2d2820f7

Base models

jordyvl/vit-base_rvl-cdip

Model card

From the Hugging Face model README

vit-base_rvl_cdip-N1K_AURC_64

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: 0.3118
Accuracy: 0.8952
Brier Loss: 0.1766
Nll: 0.8835
F1 Micro: 0.8952
F1 Macro: 0.8951
Ece: 0.0747
Aurc: 0.0206

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: 64
eval_batch_size: 64
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
No log	1.0	250	0.1770	0.8875	0.1709	1.2031	0.8875	0.8885	0.0519	0.0208
0.0228	2.0	500	0.2135	0.8852	0.1813	1.1542	0.8852	0.8853	0.0557	0.0228
0.0228	3.0	750	0.1750	0.8918	0.1729	1.0088	0.8918	0.8917	0.0628	0.0192
0.0066	4.0	1000	0.2117	0.8955	0.1697	0.9611	0.8955	0.8954	0.0655	0.0189
0.0066	5.0	1250	0.2578	0.8958	0.1714	0.9234	0.8958	0.8958	0.0690	0.0194
0.0021	6.0	1500	0.2752	0.8962	0.1730	0.9093	0.8962	0.8964	0.0709	0.0197
0.0021	7.0	1750	0.2949	0.8972	0.1748	0.8841	0.8972	0.8972	0.0708	0.0200
0.0014	8.0	2000	0.3037	0.8955	0.1755	0.8842	0.8955	0.8954	0.0739	0.0204
0.0014	9.0	2250	0.3045	0.8952	0.1764	0.8839	0.8952	0.8951	0.0741	0.0206
0.0013	10.0	2500	0.3118	0.8952	0.1766	0.8835	0.8952	0.8951	0.0747	0.0206

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