metadata

language:
  - en
license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - pittawat/letter_recognition
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-letter
    results: []

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