library_name: transformers
datasets:
- laicsiifes/flickr30k-pt-br
language:
- pt
metrics:
- bleu
- rouge
- meteor
- bertscore
base_model:
- microsoft/swin-base-patch4-window7-224
pipeline_tag: text-generation
🎉 Swin-GPorTuguese-2 for Brazilian Portuguese Image Captioning
Swin-GPorTuguese-2 model trained for image captioning on Flickr30K Portuguese (translated version using Google Translator API) at resolution 224x224 and max sequence length of 1024 tokens.
🤖 Model Description
The Swin-GPorTuguese-2 is a type of Vision Encoder Decoder which leverage the checkpoints of the Swin Transformer as encoder and the checkpoints of the GPorTuguese-2 as decoder. The encoder checkpoints come from Swin Trasnformer version pre-trained on ImageNet-1k at resolution 224x224.
The code used for training and evaluation is available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr. In this work, Swin-GPorTuguese-2 was trained together with its buddy Swin-DistilBERTimbau.
Other models evaluated didn't achieve performance as high as Swin-DistilBERTimbau and Swin-GPorTuguese-2, namely: DeiT-BERTimbau, DeiT-DistilBERTimbau, DeiT-GPorTuguese-2, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese-2.
🧑💻 How to Get Started with the Model
Use the code below to get started with the model.
import requests
from PIL import Image
from transformers import AutoTokenizer, AutoImageProcessor, VisionEncoderDecoderModel
# load a fine-tuned image captioning model and corresponding tokenizer and image processor
model = VisionEncoderDecoderModel.from_pretrained("laicsiifes/swin-gpt2-flickr30k-pt-br")
tokenizer = AutoTokenizer.from_pretrained("laicsiifes/swin-gpt2-flickr30k-pt-br")
image_processor = AutoImageProcessor.from_pretrained("laicsiifes/swin-gpt2-flickr30k-pt-br")
# perform inference on an image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
pixel_values = image_processor(image, return_tensors="pt").pixel_values
# generate caption
generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
📈 Results
The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore are abbreviated as C, B@4, RL, M and BS, respectively.
| Model | Training | Evaluation | C | B@4 | RL | M | BS |
|---|---|---|---|---|---|---|---|
| Swin-DistilBERTimbau | Flickr30K Portuguese | Flickr30K Portuguese | 66.73 | 24.65 | 39.98 | 44.71 | 72.30 |
| Swin-GPorTuguese-2 | Flickr30K Portuguese | Flickr30K Portuguese | 64.71 | 23.15 | 39.39 | 44.36 | 71.70 |
📋 BibTeX entry and citation info
Coming Soon