metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - generator
metrics:
  - accuracy
  - f1
model-index:
  - name: stool-condition-classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: stool-image
          type: generator
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.941747572815534
          - name: F1
            type: f1
            value: 0.9302325581395349

stool-condition-classification

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

Loss: 0.4237
Auroc: 0.9418
Accuracy: 0.9417
Sensitivity: 0.9091
Specificty: 0.9661
Ppv: 0.9524
Npv: 0.9344
F1: 0.9302
Model Selection: 0.9215

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Auroc	Accuracy	Sensitivity	Specificty	Ppv	Npv	F1	Model Selection
0.5076	0.98	100	0.5361	0.8538	0.7731	0.5393	0.9801	0.96	0.7061	0.6906	0.5592
0.4086	1.96	200	0.4857	0.8728	0.7836	0.6011	0.9453	0.9068	0.7280	0.7230	0.6558
0.5208	2.94	300	0.5109	0.8059	0.7599	0.6124	0.8905	0.8321	0.7218	0.7055	0.7218
0.474	3.92	400	0.5212	0.8601	0.7995	0.6180	0.9602	0.9322	0.7395	0.7432	0.6578
0.4285	4.9	500	0.4511	0.8728	0.7757	0.7472	0.8010	0.7688	0.7816	0.7578	0.9462
0.3506	5.88	600	0.4716	0.8691	0.8047	0.6798	0.9154	0.8768	0.7635	0.7658	0.7644
0.4239	6.86	700	0.5043	0.8517	0.8100	0.6685	0.9353	0.9015	0.7611	0.7677	0.7332
0.2447	7.84	800	0.5804	0.8592	0.8074	0.6910	0.9104	0.8723	0.7689	0.7712	0.7806
0.1739	8.82	900	0.6225	0.8562	0.8074	0.7135	0.8905	0.8523	0.7783	0.7768	0.8229
0.2888	9.8	1000	0.5807	0.8570	0.8047	0.7528	0.8507	0.8171	0.7953	0.7836	0.9021

Framework versions

Transformers 4.38.2
Pytorch 2.0.1
Datasets 2.14.7
Tokenizers 0.15.2