metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - image-classification
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-lr-linear
    results: []

vit-lr-linear

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

Loss: 0.4920
Accuracy: 0.8322
Precision: 0.8400
Recall: 0.8322
F1: 0.8323

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: 0.0001
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6029	0.31	100	0.6126	0.7805	0.7602	0.7805	0.7529
0.5726	0.62	200	0.6950	0.7649	0.7613	0.7649	0.7177
0.6521	0.93	300	0.5102	0.8124	0.8149	0.8124	0.8060
0.3803	1.25	400	0.6125	0.7843	0.8128	0.7843	0.7934
0.4048	1.56	500	0.5059	0.8214	0.8156	0.8214	0.8078
0.2939	1.87	600	0.6723	0.7680	0.8366	0.7680	0.7818
0.2138	2.18	700	0.6351	0.8128	0.8480	0.8128	0.8170
0.2615	2.49	800	0.4920	0.8322	0.8400	0.8322	0.8323
0.2125	2.8	900	0.5596	0.8492	0.8509	0.8492	0.8432
0.0768	3.12	1000	0.8239	0.8291	0.8500	0.8291	0.8235
0.0649	3.43	1100	0.6827	0.8367	0.8481	0.8367	0.8360
0.1382	3.74	1200	0.6838	0.8450	0.8467	0.8450	0.8399
0.0486	4.05	1300	0.6367	0.8578	0.8548	0.8578	0.8494
0.1122	4.36	1400	0.7330	0.8398	0.8368	0.8398	0.8330
0.0302	4.67	1500	0.7137	0.8450	0.8470	0.8450	0.8442
0.0462	4.98	1600	0.8198	0.8516	0.8519	0.8516	0.8456
0.0109	5.3	1700	0.8482	0.8478	0.8384	0.8478	0.8378
0.0545	5.61	1800	0.8046	0.8499	0.8547	0.8499	0.8506

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2