--- license: gemma base_model: google/paligemma-3b-pt-224 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: paligemma_race results: [] --- # FaceScanPaliGemma_Race ``` python from PIL import Image import torch from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration, BitsAndBytesConfig, TrainingArguments, Trainer model = PaliGemmaForConditionalGeneration.from_pretrained('NYUAD-ComNets/FaceScanPaliGemma_Race',torch_dtype=torch.bfloat16) input_text = "what is the race 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() ``` ## Loading in 4-bit / 8-bit ``` python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration, BitsAndBytesConfig from PIL import Image import requests import torch import time device = "cuda:0" dtype = torch.bfloat16 quantization_config = BitsAndBytesConfig(load_in_8bit=True) model = PaliGemmaForConditionalGeneration.from_pretrained( "NYUAD-ComNets/FaceScanPaliGemma_Race", quantization_config=quantization_config ).eval() processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224") prompt = "what is the race of the person in the image?" image = Image.open('image_path') model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device) input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ``` ## 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 race of face image or image with one person into seven categoris such as Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White Model Performance Accuracy: 81 %, F1 score: 79 % ## 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{aldahoul2026facescanpaligemma, title={FaceScanPaliGemma multi-agent vision language models for facial attribute recognition}, author={AlDahoul, Nouar and Tan, Myles Joshua Toledo and Kasireddy, Harishwar Reddy and Zaki, Yasir}, journal={Scientific Reports}, year={2026}, publisher={Nature Publishing Group UK London} } @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_Race](https://huggingface.co/NYUAD-ComNets/FaceScanPaliGemma_Race)}, title={FaceScanPaliGemma_Race}, 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**.