metadata

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

paligemma_race

This model is a fine-tuned version of google/paligemma-3b-pt-224 on the FairFace dataset.


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


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

input_text = "what is race of 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

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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