Vit Gpt2 Image Captioning

🚀 nlpconnect/vit - gpt2 - 圖像描述生成

本項目是一個圖像描述生成模型，由@ydshieh 在 flax 中訓練而成，這是 此模型 的 PyTorch 版本。該模型可將圖像轉化為文字描述，為圖像賦予語義信息，在圖像理解、輔助視覺障礙人士等場景具有重要價值。

🚀 快速開始

代碼示例

基礎用法

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

model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

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']

高級用法

from transformers import pipeline

image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")

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 '}]

📚 詳細文檔

圖文並茂的圖像描述生成

關於圖像描述生成的詳細解讀可參考：使用 Transformer 進行圖像描述生成的圖文詳解

📄 許可證

本項目採用 Apache - 2.0 許可證。

📞 聯繫信息

若需要任何幫助，可通過以下方式聯繫：

• Hugging Face
• Twitter
• GitHub
• LinkedIn