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--- |
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library_name: transformers |
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datasets: |
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- laicsiifes/flickr30k-pt-br |
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language: |
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- pt |
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metrics: |
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- bleu |
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- rouge |
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- meteor |
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- bertscore |
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base_model: |
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- microsoft/swin-base-patch4-window7-224 |
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- pierreguillou/gpt2-small-portuguese |
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pipeline_tag: image-to-text |
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model-index: |
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- name: Swin-GPorTuguese-2 |
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results: |
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- task: |
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name: Image Captioning |
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type: image-to-text |
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dataset: |
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name: laicsiifes/flickr30k-pt-br |
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type: flickr30k-pt-br |
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split: test |
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metrics: |
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- name: Cider-D |
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type: cider |
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value: 64.71 |
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- name: BLEU@4 |
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type: bleu |
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value: 23.15 |
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- name: ROUGE-L |
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type: rouge |
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value: 39.39 |
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- name: METEOR |
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type: meteor |
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value: 44.36 |
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- name: BERTScore |
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type: bertscore |
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value: 71.70 |
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--- |
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# 🎉 Swin-GPorTuguese-2 for Brazilian Portuguese Image Captioning |
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Swin-GPorTuguese-2 model trained for image captioning on [Flickr30K Portuguese](https://huggingface.co./datasets/laicsiifes/flickr30k-pt-br) (translated version using Google Translator API) |
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at resolution 224x224 and max sequence length of 1024 tokens. |
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## 🤖 Model Description |
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The Swin-GPorTuguese-2 is a type of Vision Encoder Decoder which leverage the checkpoints of the [Swin Transformer](https://huggingface.co./microsoft/swin-base-patch4-window7-224) |
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as encoder and the checkpoints of the [GPorTuguese-2](https://huggingface.co./pierreguillou/gpt2-small-portuguese) as decoder. |
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The encoder checkpoints come from Swin Trasnformer version pre-trained on ImageNet-1k at resolution 224x224. |
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The code used for training and evaluation is available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr. In this work, Swin-GPorTuguese-2 |
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was trained together with its buddy [Swin-DistilBERTimbau](https://huggingface.co./laicsiifes/swin-distilbert-flickr30k-pt-br). |
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Other models evaluated did not perform as well as Swin-DistilBERTimbau and Swin-GPorTuguese-2, namely: DeiT-BERTimbau, |
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DeiT-DistilBERTimbau, DeiT-GPorTuguese-2, Swin-BERTimbau, ViT-BERTimbau, ViT-DistilBERTimbau and ViT-GPorTuguese-2. |
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## 🧑💻 How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import requests |
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from PIL import Image |
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from transformers import AutoTokenizer, AutoImageProcessor, VisionEncoderDecoderModel |
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# load a fine-tuned image captioning model and corresponding tokenizer and image processor |
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model = VisionEncoderDecoderModel.from_pretrained("laicsiifes/swin-gportuguese-2") |
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tokenizer = AutoTokenizer.from_pretrained("laicsiifes/swin-gportuguese-2") |
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image_processor = AutoImageProcessor.from_pretrained("laicsiifes/swin-gportuguese-2") |
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# preprocess an image |
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url = "http://images.cocodataset.org/val2014/COCO_val2014_000000458153.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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pixel_values = image_processor(image, return_tensors="pt").pixel_values |
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# generate caption |
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generated_ids = model.generate(pixel_values) |
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generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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```python |
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import matplotlib.pyplot as plt |
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# plot image with caption |
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plt.imshow(image) |
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plt.axis("off") |
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plt.title(generated_text) |
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plt.show() |
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``` |
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## 📈 Results |
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The evaluation metrics Cider-D, BLEU@4, ROUGE-L, METEOR and BERTScore |
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(using [BERTimbau](https://huggingface.co./neuralmind/bert-base-portuguese-cased)) are abbreviated as C, B@4, RL, M and BS, respectively. |
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|Model|Dataset|Eval. Split|C|B@4|RL|M|BS| |
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|:---:|:------:|:--------:|:-----:|:----:|:-----:|:----:|:-------:| |
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|Swin-DistilBERTimbau|Flickr30K Portuguese|test|66.73|24.65|39.98|44.71|72.30| |
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|Swin-GPorTuguese-2|Flickr30K Portuguese|test|64.71|23.15|39.39|44.36|71.70| |
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## 📋 BibTeX entry and citation info |
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```bibtex |
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@inproceedings{bromonschenkel2024comparative, |
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title = "A Comparative Evaluation of Transformer-Based Vision |
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Encoder-Decoder Models for Brazilian Portuguese Image Captioning", |
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author = "Bromonschenkel, Gabriel and Oliveira, Hil{\'a}rio and |
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Paix{\~a}o, Thiago M.", |
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booktitle = "Proceedings...", |
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organization = "Conference on Graphics, Patterns and Images, 37. (SIBGRAPI)", |
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year = "2024" |
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} |
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``` |
