metadata

library_name: transformers
license: apache-2.0
base_model: ArtiSikhwal/headlight_11_12_2024_google_vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: headlight_12_12_2024_google_vit-base-patch16-224-in21k
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9014772078868953

headlight_12_12_2024_google_vit-base-patch16-224-in21k

This model is a fine-tuned version of ArtiSikhwal/headlight_11_12_2024_google_vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2587
Accuracy: 0.9015

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 512
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9995	492	0.2682	0.8973
0.1998	1.9990	984	0.2701	0.8982
0.1988	2.9985	1476	0.2708	0.8974
0.1976	4.0	1969	0.2609	0.9013
0.2131	4.9995	2461	0.2584	0.9011
0.2169	5.9970	2952	0.2587	0.9015

Framework versions

Transformers 4.46.3
Pytorch 2.4.0
Datasets 3.1.0
Tokenizers 0.20.3