---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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](https://huggingface.co/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