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

```
@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}
}
```