metadata

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

FaceScanPaliGemma_Age


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 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}
}