Image Caption Model
Model description
The model is used to generate the Chinese title of a random movie post. It is based on the BEiT and GPT2.
Training Data
The training data contains 5043 movie posts and their corresponding Chinese title which are collected by Movie-Title-Post
How to use
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
from PIL import Image
pretrained = "snzhang/FilmTitle-Beit-GPT2"
model = VisionEncoderDecoderModel.from_pretrained(pretrained)
feature_extractor = ViTFeatureExtractor.from_pretrained(pretrained)
tokenizer = AutoTokenizer.from_pretrained(pretrained)
image_path = "your image path"
image = Image.open(image_path)
if image.mode != "RGB":
image = image.convert("RGB")
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
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]
print(preds)
More Details
You can get more training details in FilmTitle-Beit-GPT2
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