vit-base_rvl_cdip-N1K_aAURC_64

bdpc/vit-base_rvl_cdip-N1K_aAURC_64

vit-base_rvl_cdip-N1K_aAURC_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 28, 2023
Updated: Oct 28, 2023
SHA: 335fc647

Base models

jordyvl/vit-base_rvl-cdip

Model card

From the Hugging Face model README

vit-base_rvl_cdip-N1K_aAURC_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: 0.4857
Accuracy: 0.8892
Brier Loss: 0.1843
Nll: 0.9506
F1 Micro: 0.8892
F1 Macro: 0.8895
Ece: 0.0837
Aurc: 0.0193

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	0.3824	0.888	0.1700	1.1756	0.888	0.8884	0.0548	0.0185
0.1403	2.0	500	0.3988	0.8925	0.1681	1.1230	0.8925	0.8936	0.0549	0.0199
0.1403	3.0	750	0.4099	0.8865	0.1756	1.0948	0.8865	0.8868	0.0672	0.0187
0.0442	4.0	1000	0.4297	0.8925	0.1747	1.0568	0.8925	0.8931	0.0685	0.0191
0.0442	5.0	1250	0.4467	0.8925	0.1775	1.0202	0.8925	0.8928	0.0734	0.0194
0.0119	6.0	1500	0.4612	0.8908	0.1808	0.9834	0.8907	0.8914	0.0772	0.0191
0.0119	7.0	1750	0.4762	0.8882	0.1845	0.9761	0.8882	0.8885	0.0827	0.0197
0.0062	8.0	2000	0.4763	0.892	0.1824	0.9652	0.892	0.8923	0.0789	0.0192
0.0062	9.0	2250	0.4854	0.8892	0.1844	0.9509	0.8892	0.8895	0.0834	0.0193
0.0051	10.0	2500	0.4857	0.8892	0.1843	0.9506	0.8892	0.8895	0.0837	0.0193

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 28, 2023
Updated: Oct 28, 2023