---
library_name: transformers
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
model-index:
- name: clip-ROCOv2-radiology-5ep
  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-ROCOv2-radiology-5ep

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: 1.4365

## 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-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.5698        | 0.6588 | 500  | 1.4979          |
| 1.0335        | 1.3175 | 1000 | 1.2915          |
| 0.9555        | 1.9763 | 1500 | 1.1798          |
| 0.644         | 2.6350 | 2000 | 1.2104          |
| 0.3687        | 3.2938 | 2500 | 1.3033          |
| 0.3659        | 3.9526 | 3000 | 1.3342          |
| 0.2289        | 4.6113 | 3500 | 1.4365          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 4.4.1
- Tokenizers 0.19.1