🚀 nlpconnect/vit - gpt2 - 圖像描述生成
這是一個圖像描述生成模型,由@ydshieh在[flax](https://github.com/huggingface/transformers/tree/main/examples/flax/image - captioning)中訓練得到,此為[該模型](https://huggingface.co/ydshieh/vit - gpt2 - coco - en - ckpts)的PyTorch版本。該模型可將圖像轉換為文本描述,為圖像賦予語義信息,在圖像理解和信息提取等方面具有重要價值。
🚀 快速開始
模型信息
屬性 |
詳情 |
標籤 |
圖像轉文本、圖像描述生成 |
許可證 |
Apache - 2.0 |
重複來源 |
nlpconnect/vit - gpt2 - 圖像描述生成 |
示例展示
- 稀樹草原
- [足球比賽](https://huggingface.co/datasets/mishig/sample_images/resolve/main/football - match.jpg)
- 機場
相關文章
[使用Transformer進行圖像描述生成的圖解](https://ankur3107.github.io/blogs/the - illustrated - image - captioning - using - transformers/)

💻 使用示例
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'])
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")
📄 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: