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vit-base-patch16-224-turkish-gpt2-medium
atasoglu/vit-base-patch16-224-turkish-gpt2-medium
vit-base-patch16-224-turkish-gpt2-medium is a image-to-text model from atasoglu. Use it when you need a caption or text from an image. It is set up for transformers. The card lists the license as apache-2.0.
This vision encoder-decoder model utilizes the google/vit-base-patch16-224 as the encoder and ytu-ce-cosmos/turkish-gpt2-medium as the decoder, and it has been fine-tuned on the flickr8k-turkish dataset to generate im…
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At a glance
- Task
- Image-to-Text
- Library
- transformers
- License
- apache-2.0
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Apr 6, 2024
- Updated
- Apr 6, 2024
- SHA
- 387faddc
Base models
Datasets
Model card
From the Hugging Face model README
vit-base-patch16-224-turkish-gpt2-medium
This vision encoder-decoder model utilizes the google/vit-base-patch16-224 as the encoder and ytu-ce-cosmos/turkish-gpt2-medium as the decoder, and it has been fine-tuned on the flickr8k-turkish dataset to generate image captions in Turkish.
Usage
import torch
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
from PIL import Image
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id = "atasoglu/vit-base-patch16-224-turkish-gpt2-medium"
img = Image.open("example.jpg")
feature_extractor = ViTImageProcessor.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = VisionEncoderDecoderModel.from_pretrained(model_id)
model.to(device)
features = feature_extractor(images=[img], return_tensors="pt")
pixel_values = features.pixel_values.to(device)
generated_captions = tokenizer.batch_decode(
model.generate(pixel_values, max_new_tokens=20),
skip_special_tokens=True,
)
print(generated_captions)
- Task
- Image-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- apache-2.0
- Languages
- tr
- Created
- Apr 6, 2024
- Updated
- Apr 6, 2024