README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - chestxrayclassification
metrics:
  - accuracy
model-index:
  - name: pneumonia-classification-model
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: chestxrayclassification
          type: chestxrayclassification
          config: full
          split: train
          args: full
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9656862745098039

pneumonia-classification-model

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

• Loss: 0.1143
• Accuracy: 0.9657

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 32

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6297	0.98	25	0.5258	0.7230
0.3781	2.0	51	0.3011	0.9154
0.2268	2.98	76	0.1981	0.9314
0.1827	4.0	102	0.1602	0.9473
0.1765	4.98	127	0.1446	0.9461
0.1508	6.0	153	0.1449	0.9510
0.1332	6.98	178	0.1510	0.9375
0.1187	8.0	204	0.1169	0.9596
0.131	8.98	229	0.1315	0.9559
0.1043	10.0	255	0.1114	0.9571
0.1022	10.98	280	0.1633	0.9375
0.0893	12.0	306	0.1167	0.9596
0.0848	12.98	331	0.0936	0.9694
0.0885	14.0	357	0.1074	0.9608
0.0928	14.98	382	0.1052	0.9645
0.0776	16.0	408	0.1116	0.9608
0.0895	16.98	433	0.1060	0.9645
0.0817	18.0	459	0.1107	0.9632
0.0766	18.98	484	0.0993	0.9669
0.0697	20.0	510	0.0938	0.9681
0.0626	20.98	535	0.1199	0.9620
0.0665	22.0	561	0.1100	0.9657
0.0613	22.98	586	0.1246	0.9620
0.054	24.0	612	0.1066	0.9645
0.0474	24.98	637	0.1100	0.9669
0.0456	26.0	663	0.1118	0.9645
0.0473	26.98	688	0.1137	0.9645
0.0543	28.0	714	0.0955	0.9632
0.0493	28.98	739	0.1300	0.9559
0.043	30.0	765	0.1229	0.9669
0.039	30.98	790	0.1125	0.9608
0.0398	31.37	800	0.1143	0.9657

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2