vit-base-chest-xray

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

• Loss: 0.0856
• Accuracy: 0.9742

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: 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: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1891	0.1307	100	0.1028	0.9665
0.2123	0.2614	200	0.1254	0.9562
0.0536	0.3922	300	0.1142	0.9691
0.0799	0.5229	400	0.1173	0.9648
0.0537	0.6536	500	0.0856	0.9742
0.0911	0.7843	600	0.2005	0.9425
0.1027	0.9150	700	0.0869	0.9708
0.1011	1.0458	800	0.1063	0.9631
0.0717	1.1765	900	0.1424	0.9588
0.0605	1.3072	1000	0.1525	0.9648
0.0573	1.4379	1100	0.0970	0.9700
0.024	1.5686	1200	0.0867	0.9751
0.0056	1.6993	1300	0.0888	0.9760
0.0051	1.8301	1400	0.1054	0.9768
0.063	1.9608	1500	0.1896	0.9571
0.002	2.0915	1600	0.1886	0.9588
0.005	2.2222	1700	0.1184	0.9734
0.0083	2.3529	1800	0.1084	0.9760
0.0013	2.4837	1900	0.0903	0.9777
0.0298	2.6144	2000	0.1023	0.9734
0.0008	2.7451	2100	0.1104	0.9768
0.0011	2.8758	2200	0.1128	0.9785
0.0006	3.0065	2300	0.1395	0.9734
0.0059	3.1373	2400	0.1419	0.9725
0.0005	3.2680	2500	0.1335	0.9777
0.0005	3.3987	2600	0.1249	0.9768
0.0007	3.5294	2700	0.1157	0.9777
0.0005	3.6601	2800	0.1202	0.9785
0.001	3.7908	2900	0.1239	0.9777
0.0004	3.9216	3000	0.1231	0.9768

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1