README.md

---
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: paligemma_gender
  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_Gender


``` python

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


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

input_text = "what is the gender 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  gender of face image or image with one person into Male and Female


## Model Performance

Accuracy: 95.8 %,  F1 score:  96 %


## 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

### Results

The model has an accuracy of %


### 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_Gender](https://huggingface.co/NYUAD-ComNets/FaceScanPaliGemma_Gender)},
      title={FaceScanPaliGemma_Gender},
      author={Nouar AlDahoul, Yasir Zaki}
}
```

## Governance & Responsible Use

The **FaceScanPaliGemma** model processes highly sensitive biometric data (facial attributes). Deployment of this model must follow **strict governance frameworks** to ensure responsible and ethical use.  

### ✅ Permitted Uses
- Academic research, benchmarking, and reproducibility studies.  
- Educational projects exploring bias, fairness, and multimodal AI.  
- Development of fairness-aware systems with proper safeguards.  

### ❌ Prohibited Uses
- **Surveillance or mass monitoring** of individuals or groups.  
- **Identity verification or authentication** without explicit and informed consent.  
- **Applications that discriminate against or marginalize** individuals or communities.  
- Use on **scraped datasets or facial images** collected without consent.  

### ⚠️ Law Enforcement Use
- Direct use in **law enforcement contexts is not recommended** due to high societal risks.  
- Risks include **bias amplification**, **wrongful identification**, and **privacy violations**.  
- If ever considered, deployment must be:  
  - Governed by **strict legal frameworks** (e.g., EU AI Act, GDPR, CCPA).  
  - Subject to **independent auditing, transparency, and accountability**.  
  - Limited to **proportional, necessary, and rights-respecting use cases**.  

### Governance Principles
1. **Access & Control** – Limit deployment to contexts with clear oversight and accountability.  
2. **Transparency** – Always disclose when and how the model is used.  
3. **Bias & Fairness Auditing** – Evaluate performance across demographic groups before deployment.  
4. **Privacy Protection** – Respect GDPR, CCPA, and local regulations; never process data without consent.  
5. **Accountability** – Establish internal review boards or ethics committees for production use.  

### Community Reporting
We encourage the community to report issues, biases, or misuse of this model through the **Hugging Face Hub discussion forum**.