transformers safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 1.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-in21k-finetuned

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: 0.0051
- Accuracy: 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 90   | 0.1809          | 0.9911   |
| No log        | 2.0   | 180  | 0.0815          | 0.9911   |
| No log        | 3.0   | 270  | 0.0542          | 0.9911   |
| No log        | 4.0   | 360  | 0.0298          | 1.0      |
| No log        | 5.0   | 450  | 0.0312          | 0.9955   |
| 0.1429        | 6.0   | 540  | 0.0235          | 1.0      |
| 0.1429        | 7.0   | 630  | 0.0196          | 1.0      |
| 0.1429        | 8.0   | 720  | 0.0154          | 1.0      |
| 0.1429        | 9.0   | 810  | 0.0145          | 1.0      |
| 0.1429        | 10.0  | 900  | 0.0125          | 1.0      |
| 0.1429        | 11.0  | 990  | 0.0115          | 1.0      |
| 0.0196        | 12.0  | 1080 | 0.0167          | 0.9955   |
| 0.0196        | 13.0  | 1170 | 0.0102          | 1.0      |
| 0.0196        | 14.0  | 1260 | 0.0093          | 1.0      |
| 0.0196        | 15.0  | 1350 | 0.0085          | 1.0      |
| 0.0196        | 16.0  | 1440 | 0.0079          | 1.0      |
| 0.0148        | 17.0  | 1530 | 0.0075          | 1.0      |
| 0.0148        | 18.0  | 1620 | 0.0074          | 1.0      |
| 0.0148        | 19.0  | 1710 | 0.0069          | 1.0      |
| 0.0148        | 20.0  | 1800 | 0.0065          | 1.0      |
| 0.0148        | 21.0  | 1890 | 0.0062          | 1.0      |
| 0.0148        | 22.0  | 1980 | 0.0062          | 1.0      |
| 0.0069        | 23.0  | 2070 | 0.0057          | 1.0      |
| 0.0069        | 24.0  | 2160 | 0.0055          | 1.0      |
| 0.0069        | 25.0  | 2250 | 0.0054          | 1.0      |
| 0.0069        | 26.0  | 2340 | 0.0053          | 1.0      |
| 0.0069        | 27.0  | 2430 | 0.0052          | 1.0      |
| 0.0055        | 28.0  | 2520 | 0.0051          | 1.0      |
| 0.0055        | 29.0  | 2610 | 0.0051          | 1.0      |
| 0.0055        | 30.0  | 2700 | 0.0051          | 1.0      |


### Framework versions

- Transformers 4.38.1
- Pytorch 1.10.0+cu111
- Datasets 2.17.1
- Tokenizers 0.15.2
