Image Caption Using ViT GPT2

🚀 圖文轉換圖像描述模型

本項目利用Transformer實現圖像描述功能，能夠根據輸入的圖像生成對應的文本描述，在圖像理解和信息提取方面具有重要價值。

🚀 快速開始

基礎環境準備

運行代碼前，請確保你已經安裝了transformers、torch和Pillow庫。你可以使用以下命令進行安裝：

pip install transformers torch pillow

代碼運行示例

手動構建模型運行

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管道運行

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 '}]

📄 許可證

本項目採用Apache-2.0許可證。