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---

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-FINALLaneClassifier-VIT50AUGMENTED
  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-FINALLaneClassifier-VIT50AUGMENTED

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: 0.0000
- 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: 50

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy                         | F1                         | Precision                         | Recall                         |
|:-------------:|:-----:|:------:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
| 0.013         | 1.0   | 2098   | 0.0503          | {'accuracy': 0.9872512808292625} | {'f1': 0.9872500637993007} | {'precision': 0.9875320438126312} | {'recall': 0.9872891423140888} |
| 0.0202        | 2.0   | 4196   | 0.0034          | {'accuracy': 0.9991659716430359} | {'f1': 0.9991659678069054} | {'precision': 0.999164877117633}  | {'recall': 0.999168448562604}  |
| 0.0007        | 3.0   | 6294   | 0.0340          | {'accuracy': 0.9864172524722984} | {'f1': 0.9864157249694355} | {'precision': 0.986738017682643}  | {'recall': 0.9864575908766928} |
| 0.0002        | 4.0   | 8392   | 0.0078          | {'accuracy': 0.9972596211128322} | {'f1': 0.9972596209572226} | {'precision': 0.9972664606608035} | {'recall': 0.9972677595628415} |
| 0.0001        | 5.0   | 10490  | 0.0051          | {'accuracy': 0.9986893840104849} | {'f1': 0.9986893803637995} | {'precision': 0.998688915375447}  | {'recall': 0.9986932763126634} |
| 0.0001        | 6.0   | 12588  | 0.0122          | {'accuracy': 0.9965447396640057} | {'f1': 0.9965447388791924} | {'precision': 0.9965582720151911} | {'recall': 0.9965550011879306} |
| 0.0002        | 7.0   | 14686  | 0.0019          | {'accuracy': 0.999523412367449}  | {'f1': 0.9995234093837869} | {'precision': 0.9995224450811844} | {'recall': 0.9995248277500595} |
| 0.0002        | 8.0   | 16784  | 0.0089          | {'accuracy': 0.9979745025616585} | {'f1': 0.9979744996862612} | {'precision': 0.9979755665421167} | {'recall': 0.9979798081321687} |
| 0.0413        | 9.0   | 18882  | 0.0082          | {'accuracy': 0.9971404742046944} | {'f1': 0.9971404741641006} | {'precision': 0.997148288973384}  | {'recall': 0.9971489665003563} |
| 0.0001        | 10.0  | 20980  | 0.0451          | {'accuracy': 0.9908256880733946} | {'f1': 0.9908253358952392} | {'precision': 0.9909645623093171} | {'recall': 0.9908529341886434} |
| 0.0           | 11.0  | 23078  | 0.0075          | {'accuracy': 0.998212796377934}  | {'f1': 0.9982127634963612} | {'precision': 0.998220079886156}  | {'recall': 0.9982088765901349} |
| 0.0           | 12.0  | 25176  | 0.0039          | {'accuracy': 0.9991659716430359} | {'f1': 0.9991659678069054} | {'precision': 0.999164877117633}  | {'recall': 0.999168448562604}  |
| 0.013         | 13.0  | 27274  | 0.0107          | {'accuracy': 0.997736208745383}  | {'f1': 0.9977362066886293} | {'precision': 0.9977383781306159} | {'recall': 0.9977422220071985} |
| 0.0537        | 14.0  | 29372  | 0.0013          | {'accuracy': 0.9996425592755868} | {'f1': 0.9996425558453789} | {'precision': 0.9996429388720207} | {'recall': 0.9996422012013773} |
| 0.0018        | 15.0  | 31470  | 0.0115          | {'accuracy': 0.9973787680209698} | {'f1': 0.997378766197631}  | {'precision': 0.997381574328435}  | {'recall': 0.9973851330141593} |
| 0.0049        | 16.0  | 33568  | 0.0040          | {'accuracy': 0.9986893840104849} | {'f1': 0.9986893803637995} | {'precision': 0.998688915375447}  | {'recall': 0.9986932763126634} |
| 0.0032        | 17.0  | 35666  | 0.0002          | {'accuracy': 0.9998808530918623} | {'f1': 0.9998808519484597} | {'precision': 0.9998812351543943} | {'recall': 0.9998804971319312} |
| 0.0002        | 18.0  | 37764  | 0.0018          | {'accuracy': 0.9994042654593114} | {'f1': 0.9994042620764765} | {'precision': 0.9994031988541419} | {'recall': 0.9994060346875742} |
| 0.0003        | 19.0  | 39862  | 0.0028          | {'accuracy': 0.9986893840104849} | {'f1': 0.9986893803637995} | {'precision': 0.998688915375447}  | {'recall': 0.9986932763126634} |
| 0.0           | 20.0  | 41960  | 0.0001          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 21.0  | 44058  | 0.0013          | {'accuracy': 0.9996425592755868} | {'f1': 0.9996425568196459} | {'precision': 0.99964174826845}   | {'recall': 0.9996436208125445} |
| 0.0005        | 22.0  | 46156  | 0.0032          | {'accuracy': 0.9990468247348981} | {'f1': 0.9990468198500874} | {'precision': 0.9990457151585668} | {'recall': 0.9990489456945351} |
| 0.0           | 23.0  | 48254  | 0.0030          | {'accuracy': 0.999523412367449}  | {'f1': 0.9995234087884033} | {'precision': 0.9995228131486541} | {'recall': 0.9995241179444757} |
| 0.0           | 24.0  | 50352  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 25.0  | 52450  | 0.0039          | {'accuracy': 0.9990468247348981} | {'f1': 0.9990468208243473} | {'precision': 0.9990458015267176} | {'recall': 0.9990496555001188} |
| 0.0           | 26.0  | 54548  | 0.0028          | {'accuracy': 0.9992851185511736} | {'f1': 0.9992851148875656} | {'precision': 0.9992840095465394} | {'recall': 0.9992872416250891} |
| 0.0           | 27.0  | 56646  | 0.0010          | {'accuracy': 0.9996425592755868} | {'f1': 0.9996425568196459} | {'precision': 0.99964174826845}   | {'recall': 0.9996436208125445} |
| 0.0002        | 28.0  | 58744  | 0.0004          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 29.0  | 60842  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 30.0  | 62940  | 0.0018          | {'accuracy': 0.999523412367449}  | {'f1': 0.9995234093837869} | {'precision': 0.9995224450811844} | {'recall': 0.9995248277500595} |
| 0.0001        | 31.0  | 65038  | 0.0020          | {'accuracy': 0.9996425592755868} | {'f1': 0.9996425558453789} | {'precision': 0.9996429388720207} | {'recall': 0.9996422012013773} |
| 0.0002        | 32.0  | 67136  | 0.0001          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 33.0  | 69234  | 0.0014          | {'accuracy': 0.9996425592755868} | {'f1': 0.9996425568196459} | {'precision': 0.99964174826845}   | {'recall': 0.9996436208125445} |
| 0.0           | 34.0  | 71332  | 0.0110          | {'accuracy': 0.9984510901942094} | {'f1': 0.9984510584424513} | {'precision': 0.9984604452865941} | {'recall': 0.9984464627151052} |
| 0.0004        | 35.0  | 73430  | 0.0009          | {'accuracy': 0.9998808530918623} | {'f1': 0.9998808521176034} | {'precision': 0.9998805256869773} | {'recall': 0.9998812069375149} |
| 0.0           | 36.0  | 75528  | 0.0009          | {'accuracy': 0.9998808530918623} | {'f1': 0.9998808521176034} | {'precision': 0.9998805256869773} | {'recall': 0.9998812069375149} |
| 0.0           | 37.0  | 77626  | 0.0002          | {'accuracy': 0.9998808530918623} | {'f1': 0.9998808521176034} | {'precision': 0.9998805256869773} | {'recall': 0.9998812069375149} |
| 0.0           | 38.0  | 79724  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 39.0  | 81822  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 40.0  | 83920  | 0.0024          | {'accuracy': 0.9994042654593114} | {'f1': 0.999404257847879}  | {'precision': 0.999406739439962}  | {'recall': 0.9994024856596558} |
| 0.0           | 41.0  | 86018  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 42.0  | 88116  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 43.0  | 90214  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 44.0  | 92312  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 45.0  | 94410  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 46.0  | 96508  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 47.0  | 98606  | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 48.0  | 100704 | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 49.0  | 102802 | 0.0000          | {'accuracy': 1.0}                | {'f1': 1.0}                | {'precision': 1.0}                | {'recall': 1.0}                |
| 0.0           | 50.0  | 104900 | 0.0000          | {'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