---
license: apache-2.0
tags:
- image-captioning
languages:
- en
datasets:
- michelecafagna26/hl
language:
- en
metrics:
- sacrebleu
- rouge
library_name: transformers
---
## ClipCap fine-tuned for Scene Image Captioning

[ClipCap](https://arxiv.org/abs/2111.09734) base trained on the [HL Dataset](https://huggingface.co/datasets/michelecafagna26/hl) for **high-level scene descriptions generation**

## Model fine-tuning 🏋️‍

We fine-tune LM + Mapping Network starting from the model pretrained on COCO 

- Trained for 9 epochs
- lr:  5e−5
- Adam optimizer
- half-precision (fp16)

## Test set metrics 🧾

    | Cider   | SacreBLEU  | Rouge-L|
    |---------|------------|--------|
    | 145.93  |   36.73    |  42.83 |

## Demo

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1redkE4W2GusU_GVDuY_R2PXbh9ZDuTqf?usp=sharing)

## Installation

```bash
pip install git+https://github.com/michelecafagna26/CLIPCap.git
```

## Download the model

```bash
git lfs install # if not installed
git clone https://huggingface.co/michelecafagna26/clipcap-base-captioning-ft-hl-scenes
```

## Model in Action 🚀


```python
from clipcap import ClipCaptionModel
from transformers import (
    GPT2Tokenizer,
    GPT2LMHeadModel,
)
import torch
import clip
import requests
from PIL import Image

model_path = "clipcap-base-captioning-ft-hl-scenes/pytorch_model.pt" # change accordingly

# load clip
device = "cuda" if torch.cuda.is_available() else "cpu"
clip_model, preprocess = clip.load("ViT-B/32", device=device, jit=False)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
prefix_length = 10

# load ClipCap
model = ClipCaptionModel(prefix_length, tokenizer=tokenizer)
model.from_pretrained(model_path)
model = model.eval()
model = model.to(device)

# load the image
img_url = 'https://datasets-server.huggingface.co/assets/michelecafagna26/hl/--/default/train/0/image/image.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')


# extract the prefix
image = preprocess(raw_image).unsqueeze(0).to(device)
with torch.no_grad():
    prefix = clip_model.encode_image(image).to(
        device, dtype=torch.float32
    )
    prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)

# generate the caption   
model.generate_beam(embed=prefix_embed)[0]


# >> "the picture is taken on the beach."
```

## BibTex and citation info

```BibTeX
@inproceedings{cafagna2023hl,
  title={{HL} {D}ataset: {V}isually-grounded {D}escription of {S}cenes, {A}ctions and
{R}ationales},
  author={Cafagna, Michele and van Deemter, Kees and Gatt, Albert},
  booktitle={Proceedings of the 16th International Natural Language Generation Conference (INLG'23)},
address = {Prague, Czech Republic},
  year={2023}
}
```