---
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: 0.967032967032967
---

<!-- 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: 0.1497
- Accuracy: 0.9670

## 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        | 0.9655  | 7    | 0.6506          | 0.5824   |
| No log        | 1.9310  | 14   | 0.3689          | 0.8352   |
| 0.5439        | 2.8966  | 21   | 0.1940          | 0.9341   |
| 0.5439        | 4.0     | 29   | 0.1185          | 0.9780   |
| 0.0961        | 4.9655  | 36   | 0.1345          | 0.9780   |
| 0.0961        | 5.9310  | 43   | 0.0828          | 0.9670   |
| 0.114         | 6.8966  | 50   | 0.3337          | 0.9451   |
| 0.114         | 8.0     | 58   | 0.1176          | 0.9670   |
| 0.0106        | 8.9655  | 65   | 0.1484          | 0.9670   |
| 0.0106        | 9.9310  | 72   | 0.0991          | 0.9780   |
| 0.0609        | 10.8966 | 79   | 0.2071          | 0.9670   |
| 0.0609        | 12.0    | 87   | 0.2575          | 0.9341   |
| 0.0112        | 12.9655 | 94   | 0.1714          | 0.9451   |
| 0.0112        | 13.9310 | 101  | 0.1918          | 0.9451   |
| 0.0147        | 14.8966 | 108  | 0.1829          | 0.9670   |
| 0.0147        | 16.0    | 116  | 0.3227          | 0.9341   |
| 0.0025        | 16.9655 | 123  | 0.1287          | 0.9780   |
| 0.0025        | 17.9310 | 130  | 0.1364          | 0.9780   |
| 0.0           | 18.8966 | 137  | 0.1446          | 0.9670   |
| 0.0           | 20.0    | 145  | 0.1487          | 0.9670   |
| 0.0           | 20.9655 | 152  | 0.1499          | 0.9670   |
| 0.0           | 21.9310 | 159  | 0.1501          | 0.9670   |
| 0.0           | 22.8966 | 166  | 0.1498          | 0.9670   |
| 0.0           | 24.0    | 174  | 0.1497          | 0.9670   |
| 0.0           | 24.1379 | 175  | 0.1497          | 0.9670   |


### Framework versions

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