vit-base-letter

pittawat/vit-base-letter

vit-base-letter is a image classification model from pittawat. 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: Mar 20, 2023
Updated: Sep 17, 2023
SHA: 7210228c

Base models

google/vit-base-patch16-224-in21k

Datasets

pittawat/letter_recognition

Spaces

pittawat/letter_recognizer

Model card

From the Hugging Face model README

vit-base-letter

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the pittawat/letter_recognition dataset. It achieves the following results on the evaluation set:

Loss: 0.0515
Accuracy: 0.9881

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: 0.0002
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5539	0.12	100	0.5576	0.9308
0.2688	0.25	200	0.2371	0.9665
0.1568	0.37	300	0.1829	0.9688
0.1684	0.49	400	0.1611	0.9662
0.1584	0.62	500	0.1340	0.9673
0.1569	0.74	600	0.1933	0.9531
0.0992	0.86	700	0.1031	0.9781
0.0573	0.98	800	0.1024	0.9781
0.0359	1.11	900	0.0950	0.9804
0.0961	1.23	1000	0.1200	0.9723
0.0334	1.35	1100	0.0995	0.975
0.0855	1.48	1200	0.0791	0.9815
0.0902	1.6	1300	0.0981	0.9765
0.0583	1.72	1400	0.1192	0.9712
0.0683	1.85	1500	0.0692	0.9846
0.1188	1.97	1600	0.0931	0.9785
0.0366	2.09	1700	0.0919	0.9804
0.0276	2.21	1800	0.0667	0.9846
0.0309	2.34	1900	0.0599	0.9858
0.0183	2.46	2000	0.0892	0.9769
0.0431	2.58	2100	0.0663	0.985
0.0424	2.71	2200	0.0643	0.9862
0.0453	2.83	2300	0.0646	0.9862
0.0528	2.95	2400	0.0550	0.985
0.0045	3.08	2500	0.0579	0.9846
0.007	3.2	2600	0.0517	0.9885
0.0048	3.32	2700	0.0584	0.9865
0.019	3.44	2800	0.0560	0.9873
0.0038	3.57	2900	0.0515	0.9881
0.0219	3.69	3000	0.0527	0.9881
0.0117	3.81	3100	0.0523	0.9888
0.0035	3.94	3200	0.0559	0.9865

Framework versions

Transformers 4.26.1
Pytorch 1.13.0
Datasets 2.1.0
Tokenizers 0.13.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Languages: en
Created: Mar 20, 2023
Updated: Sep 17, 2023