🚀 图文转换图像描述模型

本项目利用Transformer实现图像描述功能，能够根据输入的图像生成对应的文本描述，在图像理解和信息提取方面具有重要价值。

🚀 快速开始

基础环境准备

运行代码前，请确保你已经安装了transformers、torch和Pillow库。你可以使用以下命令进行安装：

pip install transformers torch pillow

代码运行示例

手动构建模型运行

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

model = VisionEncoderDecoderModel.from_pretrained("Ayansk11/Image_Caption_using_ViT_GPT2")
feature_extractor = ViTImageProcessor.from_pretrained("Ayansk11/Image_Caption_using_ViT_GPT2")
tokenizer = AutoTokenizer.from_pretrained("Ayansk11/Image_Caption_using_ViT_GPT2")

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 = feature_extractor(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(['doctor.e16ba4e4.jpg'])  # ['a woman in a hospital bed with a woman in a hospital bed']

使用transformers管道运行

from transformers import pipeline

image_to_text = pipeline("image-to-text", model="Ayansk11/Image_Caption_using_ViT_GPT2")

image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png")

# [{'generated_text': 'a soccer game with a player jumping to catch the ball '}]

📄 许可证

本项目采用Apache-2.0许可证。