---
library_name: transformers
base_model: openai/clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist
  results: []
---

<!-- 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-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist

This model is a fine-tuned version of [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0153
- Accuracy: 0.9958

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5063        | 1.0   | 422  | 0.0742          | 0.981    |
| 0.5043        | 2.0   | 844  | 0.0705          | 0.9762   |
| 0.3477        | 3.0   | 1266 | 0.0400          | 0.9883   |
| 0.3968        | 4.0   | 1688 | 0.0319          | 0.9895   |
| 0.4089        | 5.0   | 2110 | 0.0361          | 0.9893   |
| 0.3039        | 6.0   | 2532 | 0.0329          | 0.9882   |
| 0.293         | 7.0   | 2954 | 0.0244          | 0.9918   |
| 0.2723        | 8.0   | 3376 | 0.0241          | 0.9912   |
| 0.2441        | 9.0   | 3798 | 0.0164          | 0.9958   |
| 0.2394        | 10.0  | 4220 | 0.0153          | 0.9958   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1