---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ktp-kk-crop
  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: 1.0
---

<!-- 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-kk-crop

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: 0.0312
- 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: 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        | 0.8696  | 5    | 0.5871          | 0.7      |
| No log        | 1.9130  | 11   | 0.0729          | 0.9667   |
| 0.7676        | 2.9565  | 17   | 0.1986          | 0.9      |
| 0.7676        | 4.0     | 23   | 0.1610          | 0.9      |
| 0.7676        | 4.8696  | 28   | 0.0644          | 0.9667   |
| 0.2441        | 5.9130  | 34   | 0.2016          | 0.9      |
| 0.2441        | 6.9565  | 40   | 0.1530          | 0.9      |
| 0.1751        | 8.0     | 46   | 0.0412          | 1.0      |
| 0.1751        | 8.8696  | 51   | 0.0301          | 1.0      |
| 0.1751        | 9.9130  | 57   | 0.0495          | 0.9667   |
| 0.1156        | 10.9565 | 63   | 0.0283          | 1.0      |
| 0.1156        | 12.0    | 69   | 0.0214          | 1.0      |
| 0.1156        | 12.8696 | 74   | 0.1014          | 0.9667   |
| 0.1238        | 13.9130 | 80   | 0.0538          | 1.0      |
| 0.1238        | 14.9565 | 86   | 0.0477          | 1.0      |
| 0.1064        | 16.0    | 92   | 0.0105          | 1.0      |
| 0.1064        | 16.8696 | 97   | 0.0389          | 0.9667   |
| 0.1064        | 17.9130 | 103  | 0.0120          | 1.0      |
| 0.0862        | 18.9565 | 109  | 0.0183          | 1.0      |
| 0.0862        | 20.0    | 115  | 0.0259          | 1.0      |
| 0.0345        | 20.8696 | 120  | 0.0272          | 1.0      |
| 0.0345        | 21.7391 | 125  | 0.0312          | 1.0      |


### Framework versions

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