---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: document-spoof-clip
  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: 0.9857142857142858
---

<!-- 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-clip

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: 0.0225
- Accuracy: 0.9857

## 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: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8421  | 4    | 0.5305          | 0.8429   |
| No log        | 1.8947  | 9    | 0.1707          | 0.9286   |
| 0.6268        | 2.9474  | 14   | 0.3507          | 0.8429   |
| 0.6268        | 4.0     | 19   | 0.4707          | 0.8      |
| 0.2881        | 4.8421  | 23   | 0.1337          | 0.9286   |
| 0.2881        | 5.8947  | 28   | 0.1293          | 0.9286   |
| 0.2349        | 6.9474  | 33   | 0.0565          | 0.9714   |
| 0.2349        | 8.0     | 38   | 0.0676          | 0.9571   |
| 0.0907        | 8.8421  | 42   | 0.3071          | 0.9      |
| 0.0907        | 9.8947  | 47   | 0.1462          | 0.9714   |
| 0.1203        | 10.9474 | 52   | 0.0761          | 0.9714   |
| 0.1203        | 12.0    | 57   | 0.0808          | 0.9571   |
| 0.0715        | 12.8421 | 61   | 0.0204          | 0.9857   |
| 0.0715        | 13.8947 | 66   | 0.0210          | 0.9857   |
| 0.031         | 14.9474 | 71   | 0.0274          | 0.9714   |
| 0.031         | 16.0    | 76   | 0.0448          | 0.9857   |
| 0.0655        | 16.8421 | 80   | 0.0225          | 0.9857   |


### Framework versions

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