Vit Gpt2 Image Chinese Captioning

🚀 Vision Encoder-Decoder Model with ViT and GPT2

This project combines a Vision Transformer (ViT) encoder with a GPT2 decoder to support Chinese image captioning.

🚀 Quick Start

This model uses ViT to encode images and GPT2 to generate captions. It supports Chinese language processing.

✨ Features

• Encoder-Decoder Architecture: Utilizes ViT (google/vit-base-patch16-224) for image encoding and GPT2 (yuanzhoulvpi/gpt2_chinese) for caption generation.
• Chinese Support: The model is capable of generating captions in Chinese.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import (VisionEncoderDecoderModel, 
                          AutoTokenizer,ViTImageProcessor)
import torch
from PIL import Image

vision_encoder_decoder_model_name_or_path = "yuanzhoulvpi/vit-gpt2-image-chinese-captioning"#"vit-gpt2-image-chinese-captioning/checkpoint-3200"

processor = ViTImageProcessor.from_pretrained(vision_encoder_decoder_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(vision_encoder_decoder_model_name_or_path)
model = VisionEncoderDecoderModel.from_pretrained(vision_encoder_decoder_model_name_or_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}


def predict_step(image_paths):
    images = []
    for image_path in image_paths:
        i_image = Image.open(image_path)
        if i_image.mode != "RGB":
            i_image = i_image.convert(mode="RGB")

        images.append(i_image)

    pixel_values = processor(images=images, return_tensors="pt").pixel_values
    pixel_values = pixel_values.to(device)

    output_ids = model.generate(pixel_values, **gen_kwargs)

    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    preds = [pred.strip() for pred in preds]
    return preds


predict_step(['bigdata/image_data/train-1000200.jpg'])

📚 Documentation

• Training Code: You can find the training code here.

🔧 Technical Details

The model uses a Vision Transformer (ViT) as the encoder and GPT2 as the decoder. The ViT model is google/vit-base-patch16-224, and the GPT2 model is yuanzhoulvpi/gpt2_chinese. This combination allows the model to encode images and generate Chinese captions.

📄 License

This project is licensed under the MIT license.