metadata
tags:
- image-to-text
- image-captioning
language:
- ru
metrics:
- bleu
library_name: transformers
First image captioning model for russian language vit-rugpt2-image-captioning
This is an image captioning model trained on translated version (en-ru) of dataset COCO2014.
Model Details
Model was initialized google/vit-base-patch16-224-in21k for encoder and sberbank-ai/rugpt3large_based_on_gpt2 for decoder.
Metrics on test data
- Bleu: 8.672
- Bleu precision 1: 30.567
- Bleu precision 2: 7.895
- Bleu precision 3: 3.261
Sample running code
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch
from PIL import Image
model = VisionEncoderDecoderModel.from_pretrained("vit-rugpt2-image-captioning")
feature_extractor = ViTFeatureExtractor.from_pretrained("vit-rugpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("vit-rugpt2-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_caption(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_caption(['train2014/COCO_train2014_000000295442.jpg']) # ['Самолет на взлетно-посадочной полосе аэропорта.']
Sample running code using transformers pipeline
from transformers import pipeline
image_to_text = pipeline("image-to-text", model="vit-rugpt2-image-captioning")
image_to_text("train2014/COCO_train2014_000000296754.jpg") # [{'generated_text': 'Человек идет по улице с зонтом.'}]