---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ktp-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: 0.9852941176470589
---

<!-- 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. -->

# ktp-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: 0.0740
- Accuracy: 0.9853

## 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: 15

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8889  | 4    | 0.5501          | 0.8088   |
| No log        | 2.0     | 9    | 0.3671          | 0.8529   |
| 0.5611        | 2.8889  | 13   | 0.3852          | 0.8235   |
| 0.5611        | 4.0     | 18   | 0.2422          | 0.9118   |
| 0.4558        | 4.8889  | 22   | 0.3534          | 0.8824   |
| 0.4558        | 6.0     | 27   | 0.1137          | 0.9412   |
| 0.3562        | 6.8889  | 31   | 0.5266          | 0.7941   |
| 0.3562        | 8.0     | 36   | 0.1918          | 0.9118   |
| 0.1201        | 8.8889  | 40   | 0.0301          | 1.0      |
| 0.1201        | 10.0    | 45   | 0.0450          | 0.9853   |
| 0.1201        | 10.8889 | 49   | 0.0327          | 0.9853   |
| 0.0604        | 12.0    | 54   | 0.0898          | 0.9706   |
| 0.0604        | 12.8889 | 58   | 0.0789          | 0.9853   |
| 0.0322        | 13.3333 | 60   | 0.0740          | 0.9853   |


### Framework versions

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