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https://github.com/jmisilo/clip-gpt-captioning
CLIPxGPT Captioner is Image Captioning Model based on OpenAI's CLIP and GPT-2.
https://github.com/jmisilo/clip-gpt-captioning
computer-vision cv deep-learning image-caption image-caption-generator image-captioning machine-learning nlp python pytorch
Last synced: 6 days ago
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CLIPxGPT Captioner is Image Captioning Model based on OpenAI's CLIP and GPT-2.
- Host: GitHub
- URL: https://github.com/jmisilo/clip-gpt-captioning
- Owner: jmisilo
- License: mit
- Created: 2022-09-25T17:51:31.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-17T09:37:55.000Z (11 months ago)
- Last Synced: 2024-08-01T13:31:05.150Z (3 months ago)
- Topics: computer-vision, cv, deep-learning, image-caption, image-caption-generator, image-captioning, machine-learning, nlp, python, pytorch
- Language: Python
- Homepage:
- Size: 870 KB
- Stars: 104
- Watchers: 3
- Forks: 32
- Open Issues: 5
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
# CLIPxGPT Captioner
## Description
**`CLIPxGPT Captioner`** is Image Captioning Model based on [OpenAI's](https://openai.com/) [CLIP](https://openai.com/blog/clip/) and [GPT-2](https://openai.com/blog/better-language-models/). The Model uses a Mapping module to "translate" CLIP embeddings to GPT-2. The model is trained on the [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/) dataset, downloaded from [Kaggle](https://www.kaggle.com/datasets/hsankesara/flickr-image-dataset)
**The goal** of the project was to find out about the possibility of CLIP + GPT-2 connection and to check whether, with a relatively short training time and a small dataset, the model will be able to recognize situations in the pictures. The model achieved satisfactory results.
The Model uses prefixes as in the [ClipCap](https://arxiv.org/abs/2111.09734) paper. In my original idea, the length of the prefix was 1, but after reading publication, the length of the prefix was changed to 4, thanks to which the performance increased.
The Model was trained with a frozen CLIP, a fully trained Mapping Module (5-6x Transformer Encoder Layers) and with partially frozen GPT-2 (the first and last 14 layers were trained).
The training process was carried out using the [Kaggle](https://www.kaggle.com/) P100 GPU.
### Model Versions
> **Small** - [Download](https://drive.google.com/file/d/1pSQruQyg8KJq6VmzhMLFbT_VaHJMdlWF/view?usp=sharing)
>
> - Text Model - GPT-2 Small - 124M parameters
> - Mapping Module - 6x Transformer Encoder Layers
> - CLIP Base - Patch 32 model
> - 256M Parameters
> **Large** - [Download](https://drive.google.com/file/d/1Gh32arzhW06C1ZJyzcJSSfdJDi3RgWoG/view?usp=sharing)
>
> - Text Model - GPT-2 Medium - 355M parameters
> - Mapping Module - 5x Transformer Encoder Layers
> - CLIP Large - Patch 14 model
> - 736M Parameters
## Example results
## Usage
Clone repository using:
```bash
git clone https://github.com/jmisilo/clip-gpt-captioning
cd clip-gpt-captioning
```
Create environment and install requirements:
```bash
python -m venv venv
# for windows
.\venv\Scripts\activate
# for linux/mac
source venv/bin/activate
pip install -r requirements.txt
```
And run prediction:
```bash
python .\src\predict.py -I -S -C
```
### References:
- [OpenAI](https://openai.com/)
- [CLIP](https://openai.com/blog/clip/)
- [GPT-2](https://openai.com/blog/better-language-Models/)
- [ClipCap; Mokady, Ron and Hertz, Amir and Bermano, Amit H](https://arxiv.org/abs/2111.09734)
- [HuggingFace](https://huggingface.co/)