---
tags:
- generated_from_trainer
- clip
- bert
- vision-language models
model-index:
- name: output_8_clip14_cxrbert
  results: []
datasets:
- MedIR/roco
language:
- en
library_name: transformers
pipeline_tag: feature-extraction
---

# RCLIP (Clip model fine-tuned on radiology images and their captions)

This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) as an image encoder and [microsoft/BiomedVLP-CXR-BERT-general](https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general) as a text encoder on the [ROCO dataset](https://github.com/razorx89/roco-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.3388

## Heatmap

Here is the heatmap of the similarity score of the first 30 samples on the test split of the ROCO dataset of images vs their captions:
![heatmap](https://imgur.com/fPFM694.png)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 8.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7951        | 0.09  | 500   | 1.1912          |
| 0.5887        | 0.18  | 1000  | 0.9833          |
| 0.5023        | 0.28  | 1500  | 0.8459          |
| 0.4709        | 0.37  | 2000  | 0.8479          |
| 0.4484        | 0.46  | 2500  | 0.7667          |
| 0.4319        | 0.55  | 3000  | 0.8092          |
| 0.4181        | 0.64  | 3500  | 0.6964          |
| 0.4107        | 0.73  | 4000  | 0.6463          |
| 0.3723        | 0.83  | 4500  | 0.7893          |
| 0.3746        | 0.92  | 5000  | 0.6863          |
| 0.3667        | 1.01  | 5500  | 0.6910          |
| 0.3253        | 1.1   | 6000  | 0.6863          |
| 0.3274        | 1.19  | 6500  | 0.6445          |
| 0.3065        | 1.28  | 7000  | 0.5908          |
| 0.2834        | 1.38  | 7500  | 0.6138          |
| 0.293         | 1.47  | 8000  | 0.6515          |
| 0.303         | 1.56  | 8500  | 0.5806          |
| 0.2638        | 1.65  | 9000  | 0.5587          |
| 0.2593        | 1.74  | 9500  | 0.5216          |
| 0.2451        | 1.83  | 10000 | 0.5283          |
| 0.2468        | 1.93  | 10500 | 0.5001          |
| 0.2295        | 2.02  | 11000 | 0.4975          |
| 0.1953        | 2.11  | 11500 | 0.4750          |
| 0.1954        | 2.2   | 12000 | 0.4572          |
| 0.1737        | 2.29  | 12500 | 0.4731          |
| 0.175         | 2.38  | 13000 | 0.4526          |
| 0.1873        | 2.48  | 13500 | 0.4890          |
| 0.1809        | 2.57  | 14000 | 0.4210          |
| 0.1711        | 2.66  | 14500 | 0.4197          |
| 0.1457        | 2.75  | 15000 | 0.3998          |
| 0.1583        | 2.84  | 15500 | 0.3923          |
| 0.1579        | 2.94  | 16000 | 0.3823          |
| 0.1339        | 3.03  | 16500 | 0.3654          |
| 0.1164        | 3.12  | 17000 | 0.3592          |
| 0.1217        | 3.21  | 17500 | 0.3641          |
| 0.119         | 3.3   | 18000 | 0.3553          |
| 0.1151        | 3.39  | 18500 | 0.3524          |
| 0.119         | 3.49  | 19000 | 0.3452          |
| 0.102         | 3.58  | 19500 | 0.3439          |
| 0.1085        | 3.67  | 20000 | 0.3422          |
| 0.1142        | 3.76  | 20500 | 0.3396          |
| 0.1038        | 3.85  | 21000 | 0.3392          |
| 0.1143        | 3.94  | 21500 | 0.3390          |
| 0.0983        | 4.04  | 22000 | 0.3390          |
| 0.0974        | 4.13  | 22500 | 0.3388          |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3