---
library_name: transformers
datasets:
- laicsiifes/flickr30k-pt-br-human-generated
language:
- pt
metrics:
- bleu
- rouge
- meteor
- bertscore
- clipscore
base_model:
- microsoft/Phi-3-vision-128k-instruct
pipeline_tag: image-text-to-text
model-index:
- name: Phi-3-Vision-Flickr30K-Native
  results:
  - task:
      name: Image Captioning
      type: image-text-to-text
    dataset:
      name: Flickr30K Portuguese Natively Annotated
      type: laicsiifes/flickr30k-pt-br-human-generated
      split: test
    metrics:
    - name: CIDEr-D
      type: cider
      value: 72.99
    - name: BLEU@4
      type: bleu
      value: 26.74
    - name: ROUGE-L
      type: rouge
      value: 45.78
    - name: METEOR
      type: meteor
      value: 47.45
    - name: BERTScore
      type: bertscore
      value: 72.51
    - name: CLiP-Score
      type: clipscore
      value: 55.10
license: mit
---
# 🎉 Phi-3 Vision fine-tuned in Flickr30K Translated for Brazilian Portuguese Image Captioning

Phi-3 Vision (microsoft/Phi-3-vision-128k-instruct) model fine-tuned for image captioning on [Flickr30K Portuguese Natively Annotated](https://huggingface.co/datasets/laicsiifes/flickr30k-pt-br-human-generated) (annotated by Brazilian Portuguese speakers).


## 🤖 Model Description


## 🧑‍💻 How to Get Started with the Model

Use the code below to get started with the model.

- **Install libraries:**

```bash
pip install transformers==4.45.2 bitsandbytes==0.45.2 peft==0.13.2
```

- **Python code:**

```python
import requests
import torch
from PIL import Image

from transformers import AutoModelForCausalLM, AutoProcessor, BitsAndBytesConfig
from huggingface_hub import login

# Use your HuggingFace API key, since Phi-3 Vision is available through user form submission
login('hf_...')

# load a fine-tuned image captioning model, and corresponding tokenizer and image processor
model = AutoModelForCausalLM.from_pretrained(
    'microsoft/Phi-3-vision-128k-instruct',
    device_map="cuda",
    trust_remote_code=True,
    _attn_implementation='eager',
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type='nf4',
        bnb_4bit_compute_dtype=torch.bfloat16,
    )
)
model.load_adapter('laicsiifes/phi3-vision-flickr30k_pt_human_generated')
processor = AutoProcessor.from_pretrained('laicsiifes/phi3-vision-flickr30k_pt_human_generated', trust_remote_code=True)

# preprocess an image
image = Image.open(requests.get("http://images.cocodataset.org/val2014/COCO_val2014_000000458153.jpg", stream=True).raw)
text_prompt = processor.tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": f"<|image_1|>\nEscreva uma descrição em português do Brasil para a imagem com no máximo 25 palavras."
        }
    ],
    tokenize=False,
    add_generation_prompt=True
)

inputs = processor(
    text=text_prompt,
    images=image,
    return_tensors='pt'
).to('cuda:0')

# generate caption
generated_ids = model.generate(**inputs, max_new_tokens=25)
prediction = generated_ids[:, inputs['input_ids'].shape[1]:].tolist()
generated_text = processor.batch_decode(prediction, skip_special_tokens=True)[0]
```

```python
import matplotlib.pyplot as plt
# plot image with caption
plt.imshow(image)
plt.axis("off")
plt.title(generated_text)
plt.show()
```

![image/png](example.png)

## 📈 Results

The evaluation metrics: CIDEr-D, BLEU@4, ROUGE-L, METEOR, BERTScore
(using [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)), and CLIP-Score (using [CAPIVARA](https://huggingface.co/hiaac-nlp/CAPIVARA)).

| Model | #Params | CIDEr | BLEU-4 | ROUGE-L | METEOR | BERTScore | CLIP-Score |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **ViTucano 1B** | 1.53B | 69.71 | 22.67 | 43.60 | 48.63 | 72.46 | 56.14 |
| **ViTucano 2B** | 2.88B | 71.49 | 23.75 | 44.30 | **49.49** | **72.60** | 56.47 |
| **PaliGemma** | 2.92B | 55.30 | 19.41 | 39.85 | 48.96 | 70.33 | **59.96** |
| **Phi-3 V** | 4.15B | **72.99** | **26.74** | **45.78** | 47.45 | 72.51 | 55.10 |
| **LLaMa 3.2 V** | 11.70B | 69.13 | 24.79 | 43.11 | 45.99 | 72.08 | 56.38 |

## 📋 BibTeX entry and citation info

Coming soon. For now, please reference the model adapter using its Hugging Face link.