---
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: 0.989010989010989
---

<!-- 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: 0.1267
- Accuracy: 0.9890

## 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        | 1.0   | 7    | 0.5809          | 0.6374   |
| No log        | 2.0   | 14   | 1.3401          | 0.6703   |
| 0.5558        | 3.0   | 21   | 0.6458          | 0.7692   |
| 0.5558        | 4.0   | 28   | 0.3785          | 0.8681   |
| 0.1701        | 5.0   | 35   | 0.3004          | 0.9451   |
| 0.1701        | 6.0   | 42   | 0.2204          | 0.9560   |
| 0.142         | 7.0   | 49   | 0.1483          | 0.9341   |
| 0.142         | 8.0   | 56   | 0.1386          | 0.9670   |
| 0.1002        | 9.0   | 63   | 0.7714          | 0.8681   |
| 0.1002        | 10.0  | 70   | 0.2285          | 0.9341   |
| 0.0956        | 11.0  | 77   | 0.1162          | 0.9780   |
| 0.0956        | 12.0  | 84   | 0.1104          | 0.9780   |
| 0.0004        | 13.0  | 91   | 0.1722          | 0.9780   |
| 0.0004        | 14.0  | 98   | 0.2109          | 0.9780   |
| 0.0209        | 15.0  | 105  | 0.3321          | 0.9560   |
| 0.0209        | 16.0  | 112  | 0.0785          | 0.9780   |
| 0.0209        | 17.0  | 119  | 0.1525          | 0.9670   |
| 0.0014        | 18.0  | 126  | 0.1436          | 0.9670   |
| 0.0014        | 19.0  | 133  | 0.2278          | 0.9670   |
| 0.0002        | 20.0  | 140  | 0.3035          | 0.9560   |
| 0.0002        | 21.0  | 147  | 0.1239          | 0.9780   |
| 0.001         | 22.0  | 154  | 0.1211          | 0.9890   |
| 0.001         | 23.0  | 161  | 0.1253          | 0.9890   |
| 0.0           | 24.0  | 168  | 0.1265          | 0.9890   |
| 0.0           | 25.0  | 175  | 0.1267          | 0.9890   |


### Framework versions

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