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 for Image Captioning
Swin-GPorTuguese 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 is a type of Vision Encoder Decoder which leverage the checkpoints of the Swin Transformer as encoder and the checkpoints of the GPorTuguese 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 was trained together with its buddy Swin-DistilBERTimbau.
Other models evaluated didn't achieve performance as high as Swin-DistilBERTimbau and Swin-GPorTuguese, namely: DeiT-BERTimbau, DeiT-DistilBERTimbau, DeiT-GPorTuguese, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese.
🧑💻 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, ViTImageProcessor, 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 = GPT2TokenizerFast.from_pretrained("laicsiifes/swin-gpt2-flickr30k-pt-br")
image_processor = ViTImageProcessor.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 | Flickr30K Portuguese | Flickr30K Portuguese | 64.71 | 23.15 | 39.39 | 44.36 | 71.70 |
📋 BibTeX entry and citation info
Coming Soon