---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: fotocopy-ori
  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: 0.9491525423728814
---

<!-- 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. -->

# fotocopy-ori

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: 0.4776
- Accuracy: 0.9492

## 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        | 0.9231  | 3    | 0.6343          | 0.4915   |
| No log        | 1.8462  | 6    | 0.2235          | 0.9322   |
| No log        | 2.7692  | 9    | 0.1887          | 0.9492   |
| No log        | 4.0     | 13   | 0.0278          | 0.9831   |
| 0.4375        | 4.9231  | 16   | 1.6119          | 0.8475   |
| 0.4375        | 5.8462  | 19   | 0.5158          | 0.8983   |
| 0.4375        | 6.7692  | 22   | 0.0602          | 0.9661   |
| 0.4375        | 8.0     | 26   | 0.3831          | 0.9492   |
| 0.4375        | 8.9231  | 29   | 0.4555          | 0.9492   |
| 0.1245        | 9.8462  | 32   | 0.9890          | 0.9153   |
| 0.1245        | 10.7692 | 35   | 0.4632          | 0.9322   |
| 0.1245        | 12.0    | 39   | 0.5992          | 0.9322   |
| 0.1245        | 12.9231 | 42   | 0.6255          | 0.9322   |
| 0.048         | 13.8462 | 45   | 0.5156          | 0.9492   |
| 0.048         | 14.7692 | 48   | 0.6033          | 0.9492   |
| 0.048         | 16.0    | 52   | 0.5978          | 0.9492   |
| 0.048         | 16.9231 | 55   | 0.5747          | 0.9492   |
| 0.048         | 17.8462 | 58   | 0.5635          | 0.9492   |
| 0.0005        | 18.7692 | 61   | 0.5314          | 0.9492   |
| 0.0005        | 20.0    | 65   | 0.5023          | 0.9492   |
| 0.0005        | 20.9231 | 68   | 0.4886          | 0.9492   |
| 0.0005        | 21.8462 | 71   | 0.4809          | 0.9492   |
| 0.0005        | 22.7692 | 74   | 0.4779          | 0.9492   |
| 0.0           | 23.0769 | 75   | 0.4776          | 0.9492   |


### Framework versions

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