---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vit-base-patch16-224-in21k-laneclassifierasphaltconcrete-detectorVITmain50epochs
  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:
        accuracy: 1.0
    - name: F1
      type: f1
      value:
        f1: 1.0
    - name: Precision
      type: precision
      value:
        precision: 1.0
    - name: Recall
      type: recall
      value:
        recall: 1.0
---

<!-- 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. -->

# vit-base-patch16-224-in21k-laneclassifierasphaltconcrete-detectorVITmain50epochs

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Accuracy: {'accuracy': 1.0}
- F1: {'f1': 1.0}
- Precision: {'precision': 1.0}
- Recall: {'recall': 1.0}

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1                         | Precision                         | Recall                         |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
| 0.0576        | 0.9933 | 111  | 0.0139          | {'accuracy': 0.9977628635346756} | {'f1': 0.9966709613995368} | {'precision': 0.9985795454545454} | {'recall': 0.9947916666666667} |
| 0.0365        | 1.9955 | 223  | 0.0012          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0009        | 2.9978 | 335  | 0.0008          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0007        | 4.0    | 447  | 0.0007          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0006        | 4.9933 | 558  | 0.0005          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0005        | 5.9955 | 670  | 0.0005          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0005        | 6.9978 | 782  | 0.0004          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0005        | 7.9463 | 888  | 0.0004          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1