forked from ailab/vit-gpt2-image-captioning
90 lines
2.6 KiB
Markdown
90 lines
2.6 KiB
Markdown
|
---
|
||
|
|
tags:
|
||
|
|
- image-to-text
|
||
|
|
- image-captioning
|
||
|
|
license: apache-2.0
|
||
|
|
widget:
|
||
|
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
|
||
|
|
example_title: Savanna
|
||
|
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
|
||
|
|
example_title: Football Match
|
||
|
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
|
||
|
|
example_title: Airport
|
||
|
|
---
|
||
|
|
|
||
|
|
# nlpconnect/vit-gpt2-image-captioning
|
||
|
|
|
||
|
|
This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts).
|
||
|
|
|
||
|
|
|
||
|
|
# The Illustrated Image Captioning using transformers
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
* https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/
|
||
|
|
|
||
|
|
|
||
|
|
# Sample running code
|
||
|
|
|
||
|
|
```python
|
||
|
|
|
||
|
|
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']) # ['a woman in a hospital bed with a woman in a hospital bed']
|
||
|
|
|
||
|
|
```
|
||
|
|
|
||
|
|
# Sample running code using transformers pipeline
|
||
|
|
|
||
|
|
```python
|
||
|
|
|
||
|
|
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")
|
||
|
|
|
||
|
|
# [{'generated_text': 'a soccer game with a player jumping to catch the ball '}]
|
||
|
|
|
||
|
|
|
||
|
|
```
|
||
|
|
|
||
|
|
|
||
|
|
# Contact for any help
|
||
|
|
* https://huggingface.co/ankur310794
|
||
|
|
* https://twitter.com/ankur310794
|
||
|
|
* http://github.com/ankur3107
|
||
|
|
* https://www.linkedin.com/in/ankur310794
|