metadata

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

FaceScanPaliGemma_Emotion


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


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

input_text = "what is the emotion 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 AffectNet dataset. The model aims to classify the emotion of face image or image with one person into eight categoris such as 'neutral', 'happy', 'sad', 'surprise', 'fear', 'disgust', 'anger', 'contempt'

Model Performance

Accuracy: 59.4 %, F1 score: 59 %

Intended uses & limitations

This model is used for research purposes

Training and evaluation data

AffectNet 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: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
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_Emotion](https://huggingface.co/NYUAD-ComNets/FaceScanPaliGemma_Emotion)},
      title={FaceScanPaliGemma_Emotion},
      author={Nouar AlDahoul, Yasir Zaki}
}