vit-base_rvl_cdip-N1K_aAURC_256

bdpc/vit-base_rvl_cdip-N1K_aAURC_256

vit-base_rvl_cdip-N1K_aAURC_256 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 30, 2023
Updated: Oct 30, 2023
SHA: f5168ff9

Base models

jordyvl/vit-base_rvl-cdip

Model card

From the Hugging Face model README

vit-base_rvl_cdip-N1K_aAURC_256

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: 0.4424
Accuracy: 0.8932
Brier Loss: 0.1751
Nll: 1.0235
F1 Micro: 0.8932
F1 Macro: 0.8934
Ece: 0.0697
Aurc: 0.0181

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: 256
eval_batch_size: 256
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	63	0.3624	0.8972	0.1554	1.1865	0.8972	0.8975	0.0426	0.0165
No log	2.0	126	0.3717	0.8965	0.1582	1.1519	0.8965	0.8967	0.0460	0.0170
No log	3.0	189	0.3988	0.8922	0.1690	1.1118	0.8922	0.8927	0.0578	0.0177
No log	4.0	252	0.4032	0.8942	0.1677	1.0854	0.8942	0.8946	0.0590	0.0177
No log	5.0	315	0.4195	0.894	0.1706	1.0664	0.894	0.8942	0.0628	0.0179
No log	6.0	378	0.4251	0.8955	0.1711	1.0462	0.8955	0.8957	0.0637	0.0179
No log	7.0	441	0.4341	0.8925	0.1726	1.0210	0.8925	0.8927	0.0682	0.0181
0.057	8.0	504	0.4379	0.893	0.1744	1.0253	0.893	0.8932	0.0687	0.0180
0.057	9.0	567	0.4411	0.8928	0.1748	1.0200	0.8928	0.8929	0.0712	0.0181
0.057	10.0	630	0.4424	0.8932	0.1751	1.0235	0.8932	0.8934	0.0697	0.0181

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 30, 2023
Updated: Oct 30, 2023