标签:

• 图像转文本
• 图像描述生成 许可证: Apache-2.0

示例运行代码

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

model = VisionEncoderDecoderModel.from_pretrained("jaimin/image_caption")
feature_extractor = ViTFeatureExtractor.from_pretrained("jaimin/image_caption")
tokenizer = AutoTokenizer.from_pretrained("jaimin/image_caption")

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

使用transformers流水线的示例代码

from transformers import pipeline

image_to_text = pipeline("image-to-text", model="jaimin/image_caption")