---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: document-spoof
  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: 0.9767441860465116
---

<!-- 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. -->

# document-spoof

This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1105
- Accuracy: 0.9767

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9524  | 5    | 0.5211          | 0.8837   |
| No log        | 1.9048  | 10   | 0.2271          | 0.8837   |
| 0.545         | 2.8571  | 15   | 0.0975          | 0.9884   |
| 0.545         | 4.0     | 21   | 0.1020          | 0.9767   |
| 0.545         | 4.9524  | 26   | 0.3087          | 0.9535   |
| 0.472         | 5.9048  | 31   | 0.3385          | 0.8023   |
| 0.472         | 6.8571  | 36   | 0.2358          | 0.8605   |
| 0.472         | 8.0     | 42   | 0.3675          | 0.8605   |
| 0.3762        | 8.9524  | 47   | 0.1460          | 0.9535   |
| 0.3762        | 9.9048  | 52   | 0.6158          | 0.8140   |
| 0.3762        | 10.8571 | 57   | 0.3228          | 0.9186   |
| 0.1586        | 12.0    | 63   | 0.0248          | 0.9884   |
| 0.1586        | 12.9524 | 68   | 0.0639          | 0.9651   |
| 0.1586        | 13.9048 | 73   | 0.5674          | 0.8488   |
| 0.1159        | 14.8571 | 78   | 0.0291          | 0.9884   |
| 0.1159        | 16.0    | 84   | 0.0539          | 0.9884   |
| 0.1159        | 16.9524 | 89   | 0.0772          | 0.9767   |
| 0.0366        | 17.9048 | 94   | 0.0031          | 1.0      |
| 0.0366        | 18.8571 | 99   | 0.1506          | 0.9535   |
| 0.0179        | 20.0    | 105  | 0.0007          | 1.0      |
| 0.0179        | 20.9524 | 110  | 0.1427          | 0.9535   |
| 0.0179        | 21.9048 | 115  | 0.2299          | 0.9419   |
| 0.0036        | 22.8571 | 120  | 0.1373          | 0.9767   |
| 0.0036        | 23.8095 | 125  | 0.1105          | 0.9767   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1