---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: clip-vit-large-patch14-finetuned-fruits-360_vitlarge
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: fruits-360-original-size
      split: validation
      args: fruits-360-original-size
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9845857418111753
---

<!-- 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. -->

# clip-vit-large-patch14-finetuned-fruits-360_vitlarge

This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0743
- Accuracy: 0.9846

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0674        | 0.98  | 48   | 0.6958          | 0.7476   |
| 0.5475        | 1.99  | 97   | 0.4484          | 0.8542   |
| 0.4065        | 2.99  | 146  | 0.2249          | 0.9274   |
| 0.2386        | 4.0   | 195  | 0.1154          | 0.9724   |
| 0.197         | 4.92  | 240  | 0.0743          | 0.9846   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2