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---
license: apache-2.0
datasets:
- atasoglu/flickr8k-turkish
language:
- tr
metrics:
- rouge
library_name: transformers
pipeline_tag: image-to-text
tags:
- image-to-text
- image-captioning
base_model:
- google/vit-base-patch16-224
- ytu-ce-cosmos/turkish-gpt2
---
# vit-base-patch16-224-turkish-gpt2-medium
This vision encoder-decoder model utilizes the [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) as the encoder and [ytu-ce-cosmos/turkish-gpt2-medium](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-medium) as the decoder, and it has been fine-tuned on the [flickr8k-turkish](https://huggingface.co/datasets/atasoglu/flickr8k-turkish) dataset to generate image captions in Turkish.
## Usage
```py
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)
``` |