vit-base_rvl_cdip_ce

bdpc/vit-base_rvl_cdip_ce

vit-base_rvl_cdip_ce 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. --

Downloads · 30 days

2% of all-time downloads

All-time downloads

399

Public

Repo size

687 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin343 MB · 100%

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: 1144d01e

Base models

jordyvl/vit-base_rvl-cdip

Model card

From the Hugging Face model README

vit-base_rvl_cdip_ce

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: 0.5626
Accuracy: 0.8932
Brier Loss: 0.1854
Nll: 0.8898
F1 Micro: 0.8932
F1 Macro: 0.8934
Ece: 0.0831
Aurc: 0.0199

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
0.1771	1.0	500	0.4123	0.887	0.1720	1.2003	0.887	0.8872	0.0534	0.0204
0.1349	2.0	1000	0.4344	0.8895	0.1754	1.1219	0.8895	0.8900	0.0614	0.0207
0.0656	3.0	1500	0.4602	0.8852	0.1836	1.0477	0.8852	0.8856	0.0734	0.0197
0.0314	4.0	2000	0.5044	0.889	0.1851	1.0124	0.889	0.8888	0.0729	0.0230
0.0134	5.0	2500	0.5193	0.8895	0.1861	0.9779	0.8895	0.8905	0.0803	0.0207
0.0075	6.0	3000	0.5300	0.8915	0.1848	0.9515	0.8915	0.8922	0.0793	0.0203
0.0057	7.0	3500	0.5552	0.89	0.1893	0.9200	0.89	0.8897	0.0852	0.0205
0.0047	8.0	4000	0.5589	0.892	0.1871	0.9245	0.892	0.8923	0.0826	0.0198
0.0046	9.0	4500	0.5620	0.8935	0.1854	0.8987	0.8935	0.8937	0.0828	0.0199
0.0042	10.0	5000	0.5626	0.8932	0.1854	0.8898	0.8932	0.8934	0.0831	0.0199

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