---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ktp-not-ktp-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: 0.9900990099009901
---

<!-- 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-not-ktp-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: 0.0368
- Accuracy: 0.9901

## 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        | 0.9231  | 3    | 0.5958          | 0.5842   |
| No log        | 1.8462  | 6    | 0.3044          | 0.9208   |
| No log        | 2.7692  | 9    | 0.7273          | 0.7129   |
| 0.5359        | 4.0     | 13   | 0.0641          | 0.9901   |
| 0.5359        | 4.9231  | 16   | 0.0223          | 0.9901   |
| 0.5359        | 5.8462  | 19   | 0.0094          | 1.0      |
| 0.1299        | 6.7692  | 22   | 0.1034          | 0.9802   |
| 0.1299        | 8.0     | 26   | 0.0095          | 0.9901   |
| 0.1299        | 8.9231  | 29   | 0.0645          | 0.9901   |
| 0.0218        | 9.8462  | 32   | 0.1035          | 0.9901   |
| 0.0218        | 10.7692 | 35   | 0.0661          | 0.9901   |
| 0.0218        | 12.0    | 39   | 0.0406          | 0.9901   |
| 0.0419        | 12.9231 | 42   | 0.0367          | 0.9901   |
| 0.0419        | 13.8462 | 45   | 0.0368          | 0.9901   |


### Framework versions

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