---
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: 0.0159
- 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    | 0.0256          | 0.9901   |
| No log        | 1.8462  | 6    | 0.0514          | 0.9802   |
| No log        | 2.7692  | 9    | 0.1041          | 0.9604   |
| 0.1234        | 4.0     | 13   | 0.0597          | 0.9802   |
| 0.1234        | 4.9231  | 16   | 0.0736          | 0.9802   |
| 0.1234        | 5.8462  | 19   | 0.1338          | 0.9802   |
| 0.0335        | 6.7692  | 22   | 0.1006          | 0.9802   |
| 0.0335        | 8.0     | 26   | 0.0856          | 0.9802   |
| 0.0335        | 8.9231  | 29   | 0.0410          | 0.9703   |
| 0.1078        | 9.8462  | 32   | 0.1247          | 0.9802   |
| 0.1078        | 10.7692 | 35   | 0.1851          | 0.9703   |
| 0.1078        | 12.0    | 39   | 0.0078          | 1.0      |
| 0.0526        | 12.9231 | 42   | 0.0127          | 0.9901   |
| 0.0526        | 13.8462 | 45   | 0.0159          | 0.9901   |


### Framework versions

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