---
library_name: transformers
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clip-vit-base-patch32-finetuned-openai-clip-vit-base-patch32-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-patch32-finetuned-openai-clip-vit-base-patch32-mnist

This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0201
- Accuracy: 0.9937

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4813        | 1.0   | 422  | 0.1699          | 0.9447   |
| 0.4611        | 2.0   | 844  | 0.0592          | 0.9818   |
| 0.4193        | 3.0   | 1266 | 0.0584          | 0.9822   |
| 0.3782        | 4.0   | 1688 | 0.0669          | 0.9788   |
| 0.3293        | 5.0   | 2110 | 0.0349          | 0.9887   |
| 0.3383        | 6.0   | 2532 | 0.0349          | 0.9888   |
| 0.3291        | 7.0   | 2954 | 0.0381          | 0.9873   |
| 0.2783        | 8.0   | 3376 | 0.0225          | 0.9932   |
| 0.2631        | 9.0   | 3798 | 0.0217          | 0.9933   |
| 0.2815        | 10.0  | 4220 | 0.0201          | 0.9937   |


### Framework versions

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