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-v2
  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-v2

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: 0.0186
- 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    | 0.5751          | 1.0      |
| No log        | 2.0   | 2    | 0.5081          | 1.0      |
| No log        | 3.0   | 3    | 0.4654          | 1.0      |
| No log        | 4.0   | 4    | 0.4014          | 1.0      |
| No log        | 5.0   | 5    | 0.3692          | 1.0      |
| No log        | 6.0   | 6    | 0.3327          | 1.0      |
| No log        | 7.0   | 7    | 0.2937          | 1.0      |
| No log        | 8.0   | 8    | 0.2775          | 1.0      |
| No log        | 9.0   | 9    | 0.2335          | 1.0      |
| 0.4432        | 10.0  | 10   | 0.2092          | 1.0      |
| 0.4432        | 11.0  | 11   | 0.2007          | 1.0      |
| 0.4432        | 12.0  | 12   | 0.1674          | 1.0      |
| 0.4432        | 13.0  | 13   | 0.1546          | 1.0      |
| 0.4432        | 14.0  | 14   | 0.1393          | 1.0      |
| 0.4432        | 15.0  | 15   | 0.1297          | 1.0      |
| 0.4432        | 16.0  | 16   | 0.1219          | 1.0      |
| 0.4432        | 17.0  | 17   | 0.1090          | 1.0      |
| 0.4432        | 18.0  | 18   | 0.1012          | 1.0      |
| 0.4432        | 19.0  | 19   | 0.0981          | 1.0      |
| 0.1696        | 20.0  | 20   | 0.0874          | 1.0      |
| 0.1696        | 21.0  | 21   | 0.0812          | 1.0      |
| 0.1696        | 22.0  | 22   | 0.0750          | 1.0      |
| 0.1696        | 23.0  | 23   | 0.0754          | 1.0      |
| 0.1696        | 24.0  | 24   | 0.0693          | 1.0      |
| 0.1696        | 25.0  | 25   | 0.0642          | 1.0      |
| 0.1696        | 26.0  | 26   | 0.0610          | 1.0      |
| 0.1696        | 27.0  | 27   | 0.0586          | 1.0      |
| 0.1696        | 28.0  | 28   | 0.0569          | 1.0      |
| 0.1696        | 29.0  | 29   | 0.0532          | 1.0      |
| 0.0792        | 30.0  | 30   | 0.0506          | 1.0      |
| 0.0792        | 31.0  | 31   | 0.0495          | 1.0      |
| 0.0792        | 32.0  | 32   | 0.0476          | 1.0      |
| 0.0792        | 33.0  | 33   | 0.0457          | 1.0      |
| 0.0792        | 34.0  | 34   | 0.0442          | 1.0      |
| 0.0792        | 35.0  | 35   | 0.0419          | 1.0      |
| 0.0792        | 36.0  | 36   | 0.0404          | 1.0      |
| 0.0792        | 37.0  | 37   | 0.0396          | 1.0      |
| 0.0792        | 38.0  | 38   | 0.0384          | 1.0      |
| 0.0792        | 39.0  | 39   | 0.0377          | 1.0      |
| 0.049         | 40.0  | 40   | 0.0366          | 1.0      |
| 0.049         | 41.0  | 41   | 0.0370          | 1.0      |
| 0.049         | 42.0  | 42   | 0.0339          | 1.0      |
| 0.049         | 43.0  | 43   | 0.0330          | 1.0      |
| 0.049         | 44.0  | 44   | 0.0344          | 1.0      |
| 0.049         | 45.0  | 45   | 0.0324          | 1.0      |
| 0.049         | 46.0  | 46   | 0.0323          | 1.0      |
| 0.049         | 47.0  | 47   | 0.0311          | 1.0      |
| 0.049         | 48.0  | 48   | 0.0308          | 1.0      |
| 0.049         | 49.0  | 49   | 0.0294          | 1.0      |
| 0.0359        | 50.0  | 50   | 0.0297          | 1.0      |
| 0.0359        | 51.0  | 51   | 0.0289          | 1.0      |
| 0.0359        | 52.0  | 52   | 0.0285          | 1.0      |
| 0.0359        | 53.0  | 53   | 0.0280          | 1.0      |
| 0.0359        | 54.0  | 54   | 0.0270          | 1.0      |
| 0.0359        | 55.0  | 55   | 0.0265          | 1.0      |
| 0.0359        | 56.0  | 56   | 0.0266          | 1.0      |
| 0.0359        | 57.0  | 57   | 0.0261          | 1.0      |
| 0.0359        | 58.0  | 58   | 0.0268          | 1.0      |
| 0.0359        | 59.0  | 59   | 0.0255          | 1.0      |
| 0.0293        | 60.0  | 60   | 0.0255          | 1.0      |
| 0.0293        | 61.0  | 61   | 0.0246          | 1.0      |
| 0.0293        | 62.0  | 62   | 0.0256          | 1.0      |
| 0.0293        | 63.0  | 63   | 0.0247          | 1.0      |
| 0.0293        | 64.0  | 64   | 0.0241          | 1.0      |
| 0.0293        | 65.0  | 65   | 0.0241          | 1.0      |
| 0.0293        | 66.0  | 66   | 0.0234          | 1.0      |
| 0.0293        | 67.0  | 67   | 0.0236          | 1.0      |
| 0.0293        | 68.0  | 68   | 0.0228          | 1.0      |
| 0.0293        | 69.0  | 69   | 0.0233          | 1.0      |
| 0.0256        | 70.0  | 70   | 0.0227          | 1.0      |
| 0.0256        | 71.0  | 71   | 0.0227          | 1.0      |
| 0.0256        | 72.0  | 72   | 0.0230          | 1.0      |
| 0.0256        | 73.0  | 73   | 0.0222          | 1.0      |
| 0.0256        | 74.0  | 74   | 0.0220          | 1.0      |
| 0.0256        | 75.0  | 75   | 0.0221          | 1.0      |
| 0.0256        | 76.0  | 76   | 0.0219          | 1.0      |
| 0.0256        | 77.0  | 77   | 0.0215          | 1.0      |
| 0.0256        | 78.0  | 78   | 0.0210          | 1.0      |
| 0.0256        | 79.0  | 79   | 0.0209          | 1.0      |
| 0.0234        | 80.0  | 80   | 0.0212          | 1.0      |
| 0.0234        | 81.0  | 81   | 0.0212          | 1.0      |
| 0.0234        | 82.0  | 82   | 0.0206          | 1.0      |
| 0.0234        | 83.0  | 83   | 0.0210          | 1.0      |
| 0.0234        | 84.0  | 84   | 0.0204          | 1.0      |
| 0.0234        | 85.0  | 85   | 0.0205          | 1.0      |
| 0.0234        | 86.0  | 86   | 0.0204          | 1.0      |
| 0.0234        | 87.0  | 87   | 0.0203          | 1.0      |
| 0.0234        | 88.0  | 88   | 0.0200          | 1.0      |
| 0.0234        | 89.0  | 89   | 0.0203          | 1.0      |
| 0.0218        | 90.0  | 90   | 0.0196          | 1.0      |
| 0.0218        | 91.0  | 91   | 0.0199          | 1.0      |
| 0.0218        | 92.0  | 92   | 0.0198          | 1.0      |
| 0.0218        | 93.0  | 93   | 0.0196          | 1.0      |
| 0.0218        | 94.0  | 94   | 0.0195          | 1.0      |
| 0.0218        | 95.0  | 95   | 0.0198          | 1.0      |
| 0.0218        | 96.0  | 96   | 0.0197          | 1.0      |
| 0.0218        | 97.0  | 97   | 0.0193          | 1.0      |
| 0.0218        | 98.0  | 98   | 0.0195          | 1.0      |
| 0.0218        | 99.0  | 99   | 0.0194          | 1.0      |
| 0.0208        | 100.0 | 100  | 0.0192          | 1.0      |
| 0.0208        | 101.0 | 101  | 0.0190          | 1.0      |
| 0.0208        | 102.0 | 102  | 0.0188          | 1.0      |
| 0.0208        | 103.0 | 103  | 0.0191          | 1.0      |
| 0.0208        | 104.0 | 104  | 0.0193          | 1.0      |
| 0.0208        | 105.0 | 105  | 0.0193          | 1.0      |
| 0.0208        | 106.0 | 106  | 0.0190          | 1.0      |
| 0.0208        | 107.0 | 107  | 0.0191          | 1.0      |
| 0.0208        | 108.0 | 108  | 0.0186          | 1.0      |
| 0.0208        | 109.0 | 109  | 0.0188          | 1.0      |
| 0.0202        | 110.0 | 110  | 0.0187          | 1.0      |
| 0.0202        | 111.0 | 111  | 0.0191          | 1.0      |
| 0.0202        | 112.0 | 112  | 0.0188          | 1.0      |
| 0.0202        | 113.0 | 113  | 0.0185          | 1.0      |
| 0.0202        | 114.0 | 114  | 0.0188          | 1.0      |
| 0.0202        | 115.0 | 115  | 0.0183          | 1.0      |
| 0.0202        | 116.0 | 116  | 0.0187          | 1.0      |
| 0.0202        | 117.0 | 117  | 0.0185          | 1.0      |
| 0.0202        | 118.0 | 118  | 0.0184          | 1.0      |
| 0.0202        | 119.0 | 119  | 0.0188          | 1.0      |
| 0.0197        | 120.0 | 120  | 0.0185          | 1.0      |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
