README.md

---
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: paligemma_age
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# FaceScanPaliGemma_Age


``` python

from PIL import Image
import torch
from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration, BitsAndBytesConfig, TrainingArguments, Trainer


model = PaliGemmaForConditionalGeneration.from_pretrained('NYUAD-ComNets/FaceScanPaliGemma_Age',torch_dtype=torch.bfloat16)

input_text = "what is the age group of the person in the image?"

processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-pt-224")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model.to(device)


input_image = Image.open('image_path')
inputs = processor(text=input_text, images=input_image, padding="longest", do_convert_rgb=True, return_tensors="pt").to(device)
inputs = inputs.to(dtype=model.dtype)
      
with torch.no_grad():
          output = model.generate(**inputs, max_length=500)
result=processor.decode(output[0], skip_special_tokens=True)[len(input_text):].strip()


```

## Model description

This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the FairFace dataset. 
The model aims to classify the age of face image or image with one person into five groups such as from 0 to 9, from 10 to 19, from 20 to 39, from 40 ro 59, More than 60


## Model Performance
Accuracy: 80 %,   F1 score: 74 %


## Intended uses & limitations

This model is used for research purposes

## Training and evaluation data

FairFace dataset was used for training and validating the model

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.2+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1


# BibTeX entry and citation info

```
@article{aldahoul2024exploring,
  title={Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age},
  author={AlDahoul, Nouar and Tan, Myles Joshua Toledo and Kasireddy, Harishwar Reddy and Zaki, Yasir},
  journal={arXiv preprint arXiv:2410.24148},
  year={2024}
}


@misc{ComNets,
      url={https://huggingface.co/NYUAD-ComNets/FaceScanPaliGemma_Age](https://huggingface.co/NYUAD-ComNets/FaceScanPaliGemma_Age)},
      title={FaceScanPaliGemma_Age},
      author={Nouar AlDahoul, Yasir Zaki}
}