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flax-vision-encoder-decoder-vit-gpt2-coco-en

ydshieh / flax-vision-encoder-decoder-vit-gpt2-coco-en

Run on huggingface.co
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Total runs: 0
24-hour runs: 0
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Model's Last Updated: Tháng 12 29 2021

Introduction of flax-vision-encoder-decoder-vit-gpt2-coco-en

Model Details of flax-vision-encoder-decoder-vit-gpt2-coco-en

Example

The model is by no means a state-of-the-art model, but nevertheless produces reasonable image captioning results. It was mainly fine-tuned as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.

The model can be used as follows: 


import requests
from PIL import Image

from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel

loc = "ydshieh/flax-vit-gpt2-coco-en"

feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)

# We will verify our results on an image of cute cats
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
with Image.open(requests.get(url, stream=True).raw) as img:
    pixel_values = feature_extractor(images=img, return_tensors="np").pixel_values

def generate_step(pixel_values):

    output_ids = model.generate(pixel_values, max_length=16, num_beams=4).sequences
    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    preds = [pred.strip() for pred in preds]

    return preds

preds = generate_step(pixel_values)
print(preds)

# should produce
# ['a cat laying on top of a couch next to another cat']

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More Information About flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co Model

flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co

flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co is an AI model on huggingface.co that provides flax-vision-encoder-decoder-vit-gpt2-coco-en's model effect (), which can be used instantly with this ydshieh flax-vision-encoder-decoder-vit-gpt2-coco-en model. huggingface.co supports a free trial of the flax-vision-encoder-decoder-vit-gpt2-coco-en model, and also provides paid use of the flax-vision-encoder-decoder-vit-gpt2-coco-en. Support call flax-vision-encoder-decoder-vit-gpt2-coco-en model through api, including Node.js, Python, http.

flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co Url

https://huggingface.co/ydshieh/flax-vision-encoder-decoder-vit-gpt2-coco-en

ydshieh flax-vision-encoder-decoder-vit-gpt2-coco-en online free

flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co is an online trial and call api platform, which integrates flax-vision-encoder-decoder-vit-gpt2-coco-en's modeling effects, including api services, and provides a free online trial of flax-vision-encoder-decoder-vit-gpt2-coco-en, you can try flax-vision-encoder-decoder-vit-gpt2-coco-en online for free by clicking the link below.

ydshieh flax-vision-encoder-decoder-vit-gpt2-coco-en online free url in huggingface.co:

https://huggingface.co/ydshieh/flax-vision-encoder-decoder-vit-gpt2-coco-en

flax-vision-encoder-decoder-vit-gpt2-coco-en install

flax-vision-encoder-decoder-vit-gpt2-coco-en is an open source model from GitHub that offers a free installation service, and any user can find flax-vision-encoder-decoder-vit-gpt2-coco-en on GitHub to install. At the same time, huggingface.co provides the effect of flax-vision-encoder-decoder-vit-gpt2-coco-en install, users can directly use flax-vision-encoder-decoder-vit-gpt2-coco-en installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

flax-vision-encoder-decoder-vit-gpt2-coco-en install url in huggingface.co:

https://huggingface.co/ydshieh/flax-vision-encoder-decoder-vit-gpt2-coco-en

Url of flax-vision-encoder-decoder-vit-gpt2-coco-en

flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co Url

Provider of flax-vision-encoder-decoder-vit-gpt2-coco-en huggingface.co

ydshieh
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