---
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: paligemma_Malaysian_plate_recognition
  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. -->

# paligemma_Malaysian_plate_recognition

This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the Malaysian license plate dataset.



``` python

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

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

input_text ="extract the text from 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()

```


### 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


### 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{aldahoul2024advancingvehicleplaterecognition,
      title={Advancing Vehicle Plate Recognition: Multitasking Visual Language Models with VehiclePaliGemma}, 
      author={Nouar AlDahoul and Myles Joshua Toledo Tan and Raghava Reddy Tera and Hezerul Abdul Karim and Chee How Lim and Manish Kumar Mishra and Yasir Zaki},
      year={2024},
      eprint={2412.14197},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.14197}, 
}