---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ktp-crop-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: 0.9864864864864865
---

<!-- 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-crop-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: 0.1223
- Accuracy: 0.9865

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.96  | 6    | 0.8954          | 0.5270   |
| 0.7112        | 1.92  | 12   | 0.6729          | 0.5405   |
| 0.7112        | 2.88  | 18   | 0.6407          | 0.7297   |
| 0.4413        | 4.0   | 25   | 0.1279          | 0.9459   |
| 0.0935        | 4.96  | 31   | 0.1436          | 0.9730   |
| 0.0935        | 5.92  | 37   | 0.0021          | 1.0      |
| 0.0697        | 6.88  | 43   | 0.2862          | 0.9459   |
| 0.161         | 8.0   | 50   | 0.0843          | 0.9595   |
| 0.161         | 8.96  | 56   | 0.2255          | 0.9459   |
| 0.0061        | 9.92  | 62   | 0.4678          | 0.9054   |
| 0.0061        | 10.88 | 68   | 0.3299          | 0.9189   |
| 0.0309        | 12.0  | 75   | 0.5189          | 0.9189   |
| 0.0025        | 12.96 | 81   | 0.0850          | 0.9865   |
| 0.0025        | 13.92 | 87   | 0.0720          | 0.9865   |
| 0.0042        | 14.88 | 93   | 0.0745          | 0.9865   |
| 0.0002        | 16.0  | 100  | 0.0869          | 0.9865   |
| 0.0002        | 16.96 | 106  | 0.0895          | 0.9865   |
| 0.0001        | 17.92 | 112  | 0.1127          | 0.9865   |
| 0.0001        | 18.88 | 118  | 0.1219          | 0.9865   |
| 0.0           | 19.2  | 120  | 0.1223          | 0.9865   |


### Framework versions

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