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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.
Source du modèle
Description de la source
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:
In PyTorch
Sources
1 sourceVérifié 17 sept.
Artefacts du modèle
4 artefactsExtraits de sources
2 extraits
import torch
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = VisionEncoderDecoderModel.from_pretrained(loc)
model.eval()
def predict(image):
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
with torch.no_grad():
output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
# 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 image:
preds = predict(image)
print(preds)
# should produce
# ['a cat laying on top of a couch next to another cat']
In Flax
import jax
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
gen_kwargs = {"max_length": 16, "num_beams": 4}
# This takes sometime when compiling the first time, but the subsequent inference will be much faster
@jax.jit
def generate(pixel_values):
output_ids = model.generate(pixel_values, **gen_kwargs).sequences
return output_ids
def predict(image):
pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values
output_ids = generate(pixel_values)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
# 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 image:
preds = predict(image)
print(preds)
# should produce
# ['a cat laying on top of a couch next to another cat']
pytorch_model.bin
bin · 937 MB · SHA-256 e42892c4e6b5…5575 · Hugging Face
--- tags: - image-to-text widget: - 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/dog-cat.jpg example_title: Dog & Cat --- ## 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: **In PyTorch** ```python import torch import requests from PIL import Image from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel loc = "ydshieh/vit-gpt2-coco-en" feature_extractor = ViTFeatureExtractor.from_pretrained(loc) tokenizer = AutoTokenizer.from_pretrained(loc) model = VisionEncoderDecoderModel.from_pretrained(loc) model.eval() def predict(image): pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values with torch.no_grad(): output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) preds = [pred.strip() for pred in preds] return preds # 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 image: preds = predict(image) print(preds) # should produce # ['a cat laying on top of a couch next to another cat'] ``` **In Flax** ```python import jax import requests from PIL import Image from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel loc = "ydshieh/vit-gpt2-coco-en" feature_extractor = ViTFeatureExtractor.from_pretrained(loc) tokenizer = AutoTokenizer.from_pretrained(loc) model = FlaxVisionEncoderDecoderModel.from_pretrained(loc) gen_kwargs = {"max_length": 16, "num_beams": 4} # This takes sometime when compiling the first time, but the subsequent inference will be much faster @jax.jit def generate(pixel_values): output_ids = model.generate(pixel_values, **gen_kwargs).sequences return output_ids def predict(image): pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values output_ids = generate(pixel_values) preds = tokenizer.batch_decode(output_ids, ski...
Source context: 12529 downloads · 39 likes · Pipeline image-to-text · Library transformers · Repo ydshieh/vit-gpt2-coco-en