metadata
tags:
- image-to-text
- image-captioning
license: apache-2.0
widget:
- src: >-
https://pixabay.com/get/ga187b8f146a9fa30b1f553d63fa94271e023868cd247fbad7ce02b6ffb5718a52fc04809be440f997f57dad90614dde2e9821edf8e628925f0042c6584fc04ec809421a040e3bc9561324249ab6e09c4_1280.jpg
example_title: Horse Riding
- src: https://static1.bigstockphoto.com/6/8/2/large1500/286059499.jpg
example_title: Bicycle
This is an image captioning model training by Zayn
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
model = VisionEncoderDecoderModel.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
feature_extractor = ViTFeatureExtractor.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
tokenizer = AutoTokenizer.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
max_length = 20
num_beams = 8
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(['Image URL.jpg'])