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---
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
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