transformers tensorboard safetensors vit image-classification vision 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:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-gabor-detection-v3
  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: 1.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-gabor-detection-v3

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: 0.4139
- 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: 200
- eval_batch_size: 200
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 120.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 0.6629          | 0.5      |
| No log        | 2.0   | 2    | 0.6564          | 0.5      |
| No log        | 3.0   | 3    | 0.6496          | 0.5      |
| No log        | 4.0   | 4    | 0.6428          | 0.5      |
| No log        | 5.0   | 5    | 0.6362          | 0.5      |
| No log        | 6.0   | 6    | 0.6296          | 0.5      |
| No log        | 7.0   | 7    | 0.6232          | 0.5      |
| No log        | 8.0   | 8    | 0.6172          | 0.5      |
| No log        | 9.0   | 9    | 0.6113          | 1.0      |
| 0.509         | 10.0  | 10   | 0.6058          | 1.0      |
| 0.509         | 11.0  | 11   | 0.6005          | 1.0      |
| 0.509         | 12.0  | 12   | 0.5950          | 1.0      |
| 0.509         | 13.0  | 13   | 0.5892          | 1.0      |
| 0.509         | 14.0  | 14   | 0.5832          | 1.0      |
| 0.509         | 15.0  | 15   | 0.5765          | 1.0      |
| 0.509         | 16.0  | 16   | 0.5699          | 1.0      |
| 0.509         | 17.0  | 17   | 0.5630          | 1.0      |
| 0.509         | 18.0  | 18   | 0.5562          | 1.0      |
| 0.509         | 19.0  | 19   | 0.5494          | 1.0      |
| 0.248         | 20.0  | 20   | 0.5426          | 1.0      |
| 0.248         | 21.0  | 21   | 0.5360          | 1.0      |
| 0.248         | 22.0  | 22   | 0.5295          | 1.0      |
| 0.248         | 23.0  | 23   | 0.5231          | 1.0      |
| 0.248         | 24.0  | 24   | 0.5175          | 1.0      |
| 0.248         | 25.0  | 25   | 0.5126          | 1.0      |
| 0.248         | 26.0  | 26   | 0.5079          | 1.0      |
| 0.248         | 27.0  | 27   | 0.5034          | 1.0      |
| 0.248         | 28.0  | 28   | 0.4991          | 1.0      |
| 0.248         | 29.0  | 29   | 0.4949          | 1.0      |
| 0.119         | 30.0  | 30   | 0.4908          | 1.0      |
| 0.119         | 31.0  | 31   | 0.4868          | 1.0      |
| 0.119         | 32.0  | 32   | 0.4833          | 1.0      |
| 0.119         | 33.0  | 33   | 0.4803          | 1.0      |
| 0.119         | 34.0  | 34   | 0.4777          | 1.0      |
| 0.119         | 35.0  | 35   | 0.4751          | 1.0      |
| 0.119         | 36.0  | 36   | 0.4727          | 1.0      |
| 0.119         | 37.0  | 37   | 0.4704          | 1.0      |
| 0.119         | 38.0  | 38   | 0.4681          | 1.0      |
| 0.119         | 39.0  | 39   | 0.4658          | 1.0      |
| 0.0692        | 40.0  | 40   | 0.4635          | 1.0      |
| 0.0692        | 41.0  | 41   | 0.4612          | 1.0      |
| 0.0692        | 42.0  | 42   | 0.4588          | 1.0      |
| 0.0692        | 43.0  | 43   | 0.4564          | 1.0      |
| 0.0692        | 44.0  | 44   | 0.4542          | 1.0      |
| 0.0692        | 45.0  | 45   | 0.4522          | 1.0      |
| 0.0692        | 46.0  | 46   | 0.4504          | 1.0      |
| 0.0692        | 47.0  | 47   | 0.4488          | 1.0      |
| 0.0692        | 48.0  | 48   | 0.4474          | 1.0      |
| 0.0692        | 49.0  | 49   | 0.4463          | 1.0      |
| 0.0487        | 50.0  | 50   | 0.4453          | 1.0      |
| 0.0487        | 51.0  | 51   | 0.4444          | 1.0      |
| 0.0487        | 52.0  | 52   | 0.4435          | 1.0      |
| 0.0487        | 53.0  | 53   | 0.4427          | 1.0      |
| 0.0487        | 54.0  | 54   | 0.4419          | 1.0      |
| 0.0487        | 55.0  | 55   | 0.4410          | 1.0      |
| 0.0487        | 56.0  | 56   | 0.4402          | 1.0      |
| 0.0487        | 57.0  | 57   | 0.4394          | 1.0      |
| 0.0487        | 58.0  | 58   | 0.4385          | 1.0      |
| 0.0487        | 59.0  | 59   | 0.4375          | 1.0      |
| 0.0374        | 60.0  | 60   | 0.4366          | 1.0      |
| 0.0374        | 61.0  | 61   | 0.4356          | 1.0      |
| 0.0374        | 62.0  | 62   | 0.4347          | 1.0      |
| 0.0374        | 63.0  | 63   | 0.4338          | 1.0      |
| 0.0374        | 64.0  | 64   | 0.4328          | 1.0      |
| 0.0374        | 65.0  | 65   | 0.4319          | 1.0      |
| 0.0374        | 66.0  | 66   | 0.4311          | 1.0      |
| 0.0374        | 67.0  | 67   | 0.4302          | 1.0      |
| 0.0374        | 68.0  | 68   | 0.4294          | 1.0      |
| 0.0374        | 69.0  | 69   | 0.4286          | 1.0      |
| 0.0321        | 70.0  | 70   | 0.4278          | 1.0      |
| 0.0321        | 71.0  | 71   | 0.4271          | 1.0      |
| 0.0321        | 72.0  | 72   | 0.4264          | 1.0      |
| 0.0321        | 73.0  | 73   | 0.4257          | 1.0      |
| 0.0321        | 74.0  | 74   | 0.4251          | 1.0      |
| 0.0321        | 75.0  | 75   | 0.4245          | 1.0      |
| 0.0321        | 76.0  | 76   | 0.4239          | 1.0      |
| 0.0321        | 77.0  | 77   | 0.4233          | 1.0      |
| 0.0321        | 78.0  | 78   | 0.4228          | 1.0      |
| 0.0321        | 79.0  | 79   | 0.4223          | 1.0      |
| 0.0285        | 80.0  | 80   | 0.4219          | 1.0      |
| 0.0285        | 81.0  | 81   | 0.4215          | 1.0      |
| 0.0285        | 82.0  | 82   | 0.4211          | 1.0      |
| 0.0285        | 83.0  | 83   | 0.4206          | 1.0      |
| 0.0285        | 84.0  | 84   | 0.4201          | 1.0      |
| 0.0285        | 85.0  | 85   | 0.4197          | 1.0      |
| 0.0285        | 86.0  | 86   | 0.4192          | 1.0      |
| 0.0285        | 87.0  | 87   | 0.4189          | 1.0      |
| 0.0285        | 88.0  | 88   | 0.4185          | 1.0      |
| 0.0285        | 89.0  | 89   | 0.4182          | 1.0      |
| 0.0268        | 90.0  | 90   | 0.4179          | 1.0      |
| 0.0268        | 91.0  | 91   | 0.4176          | 1.0      |
| 0.0268        | 92.0  | 92   | 0.4173          | 1.0      |
| 0.0268        | 93.0  | 93   | 0.4170          | 1.0      |
| 0.0268        | 94.0  | 94   | 0.4168          | 1.0      |
| 0.0268        | 95.0  | 95   | 0.4165          | 1.0      |
| 0.0268        | 96.0  | 96   | 0.4163          | 1.0      |
| 0.0268        | 97.0  | 97   | 0.4161          | 1.0      |
| 0.0268        | 98.0  | 98   | 0.4159          | 1.0      |
| 0.0268        | 99.0  | 99   | 0.4157          | 1.0      |
| 0.0249        | 100.0 | 100  | 0.4155          | 1.0      |
| 0.0249        | 101.0 | 101  | 0.4154          | 1.0      |
| 0.0249        | 102.0 | 102  | 0.4152          | 1.0      |
| 0.0249        | 103.0 | 103  | 0.4151          | 1.0      |
| 0.0249        | 104.0 | 104  | 0.4150          | 1.0      |
| 0.0249        | 105.0 | 105  | 0.4148          | 1.0      |
| 0.0249        | 106.0 | 106  | 0.4147          | 1.0      |
| 0.0249        | 107.0 | 107  | 0.4146          | 1.0      |
| 0.0249        | 108.0 | 108  | 0.4145          | 1.0      |
| 0.0249        | 109.0 | 109  | 0.4144          | 1.0      |
| 0.0242        | 110.0 | 110  | 0.4144          | 1.0      |
| 0.0242        | 111.0 | 111  | 0.4143          | 1.0      |
| 0.0242        | 112.0 | 112  | 0.4142          | 1.0      |
| 0.0242        | 113.0 | 113  | 0.4141          | 1.0      |
| 0.0242        | 114.0 | 114  | 0.4141          | 1.0      |
| 0.0242        | 115.0 | 115  | 0.4140          | 1.0      |
| 0.0242        | 116.0 | 116  | 0.4140          | 1.0      |
| 0.0242        | 117.0 | 117  | 0.4139          | 1.0      |
| 0.0242        | 118.0 | 118  | 0.4139          | 1.0      |
| 0.0242        | 119.0 | 119  | 0.4139          | 1.0      |
| 0.0292        | 120.0 | 120  | 0.4139          | 1.0      |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.4.0
- Tokenizers 0.15.0
