---
library_name: transformers
base_model: openai/clip-vit-large-patch14-336
tags:
- generated_from_trainer
model-index:
- name: clip-finetuned-csu-p14-336-e3l58-l
  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-finetuned-csu-p14-336-e3l58-l

This model is a fine-tuned version of [openai/clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9259

## 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-08
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.3758        | 0.0921 | 500   | 1.4188          |
| 0.4108        | 0.1842 | 1000  | 1.3514          |
| 0.4335        | 0.2763 | 1500  | 1.2895          |
| 0.3436        | 0.3685 | 2000  | 1.2415          |
| 0.366         | 0.4606 | 2500  | 1.1949          |
| 0.3191        | 0.5527 | 3000  | 1.1560          |
| 0.2779        | 0.6448 | 3500  | 1.1234          |
| 0.2942        | 0.7369 | 4000  | 1.0993          |
| 0.2615        | 0.8290 | 4500  | 1.0805          |
| 0.2715        | 0.9211 | 5000  | 1.0615          |
| 0.2509        | 1.0133 | 5500  | 1.0462          |
| 0.2653        | 1.1054 | 6000  | 1.0306          |
| 0.2199        | 1.1975 | 6500  | 1.0183          |
| 0.1844        | 1.2896 | 7000  | 1.0079          |
| 0.2143        | 1.3817 | 7500  | 0.9970          |
| 0.2011        | 1.4738 | 8000  | 0.9887          |
| 0.1995        | 1.5660 | 8500  | 0.9807          |
| 0.2493        | 1.6581 | 9000  | 0.9733          |
| 0.2192        | 1.7502 | 9500  | 0.9669          |
| 0.1882        | 1.8423 | 10000 | 0.9616          |
| 0.2193        | 1.9344 | 10500 | 0.9551          |
| 0.2148        | 2.0265 | 11000 | 0.9495          |
| 0.1975        | 2.1186 | 11500 | 0.9449          |
| 0.1791        | 2.2108 | 12000 | 0.9409          |
| 0.2057        | 2.3029 | 12500 | 0.9382          |
| 0.2037        | 2.3950 | 13000 | 0.9352          |
| 0.2011        | 2.4871 | 13500 | 0.9318          |
| 0.1551        | 2.5792 | 14000 | 0.9295          |
| 0.1565        | 2.6713 | 14500 | 0.9279          |
| 0.1963        | 2.7634 | 15000 | 0.9268          |
| 0.1823        | 2.8556 | 15500 | 0.9262          |
| 0.1854        | 2.9477 | 16000 | 0.9259          |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 1.12.1
- Datasets 2.21.0
- Tokenizers 0.19.1