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---
license: apache-2.0
tags:
- image-captioning
languages:
- en
datasets:
- michelecafagna26/hl-narratives
language:
- en
metrics:
- sacrebleu
- rouge
library_name: transformers
---
## ClipCap fine-tuned for Narrative Image Captioning
[ClipCap](https://arxiv.org/abs/2111.09734) base trained on the [HL Narratives](https://huggingface.co/datasets/michelecafagna26/hl-narratives) for **high-level narrative descriptions generation**
## Model fine-tuning 🏋️
We fine-tune LM + Mapping Network starting from the model pretrained on COCO
- Trained for 3 epochs
- lr: 5e−5
- Adam optimizer
- half-precision (fp16)
## Test set metrics 🧾
| Cider | SacreBLEU | Rouge-L|
|--------|------------|--------|
| 63.91 | 8.15 | 24.53 |
## Demo
[](https://colab.research.google.com/drive/1xcaJOxaAp8TRd8a6x1XnAptVjHQRv3Zj?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-narratives
```
## 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-narratives/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 = '/static-proxy?url=https%3A%2F%2Fdatasets-server.huggingface.co%2Fassets%2Fmichelecafagna26%2Fhl-narratives%2F--%2Fdefault%2Ftrain%2F3%2Fimage%2Fimage.jpg%26%23x27%3B
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]
# >> "He is riding a skateboard in a skate park, he wants to skate."
```
## 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}
}
``` |