metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8518518518518519
          - name: Recall
            type: recall
            value: 0.8518518518518519
          - name: F1
            type: f1
            value: 0.8508141812977819
          - name: Precision
            type: precision
            value: 0.8576385720576808

vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter

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

Loss: 0.3278
Accuracy: 0.8519
Recall: 0.8519
F1: 0.8508
Precision: 0.8576

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	F1	Precision
No log	1.0	1175	0.5572	0.8076	0.8076	0.7937	0.8043
No log	2.0	2350	0.4673	0.8284	0.8284	0.8271	0.8347
No log	3.0	3525	0.4109	0.8344	0.8344	0.8301	0.8367
No log	4.0	4700	0.3984	0.8382	0.8382	0.8339	0.8375
No log	5.0	5875	0.3886	0.8412	0.8412	0.8398	0.8467
No log	6.0	7050	0.3520	0.8493	0.8493	0.8481	0.8519
No log	7.0	8225	0.4229	0.8416	0.8416	0.8399	0.8512
No log	8.0	9400	0.3140	0.8612	0.8612	0.8600	0.8656
No log	9.0	10575	0.3399	0.8421	0.8421	0.8403	0.8464
0.4263	10.0	11750	0.3399	0.8476	0.8476	0.8468	0.8536

Framework versions

Transformers 4.40.1
Pytorch 2.2.0a0+81ea7a4
Datasets 2.19.0
Tokenizers 0.19.1