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: 0.8339719029374202
          - name: Recall
            type: recall
            value: 0.8339719029374202
          - name: F1
            type: f1
            value: 0.8319571049551264
          - name: Precision
            type: precision
            value: 0.8325133593723552

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: 0.3507
Accuracy: 0.8340
Recall: 0.8340
F1: 0.8320
Precision: 0.8325

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
gradient_accumulation_steps: 4
total_train_batch_size: 32
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	0.9974	293	0.6168	0.7923	0.7923	0.7737	0.7684
No log	1.9983	587	0.4599	0.8110	0.8110	0.8056	0.8085
No log	2.9991	881	0.4305	0.8233	0.8233	0.8211	0.8250
No log	4.0	1175	0.3966	0.8365	0.8365	0.8323	0.8452
No log	4.9974	1468	0.4100	0.8221	0.8221	0.8195	0.8219
No log	5.9983	1762	0.3890	0.8412	0.8412	0.8375	0.8466
No log	6.9991	2056	0.3659	0.8357	0.8357	0.8335	0.8386
No log	8.0	2350	0.3562	0.8395	0.8395	0.8379	0.8403
No log	8.9974	2643	0.3613	0.8382	0.8382	0.8373	0.8391
0.4339	9.9745	2930	0.3405	0.8455	0.8455	0.8447	0.8467

Framework versions

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