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--- |
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license: gemma |
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base_model: google/paligemma-3b-pt-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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model-index: |
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- name: paligemma_Malaysian_plate_recognition |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# paligemma_Malaysian_plate_recognition |
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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. |
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``` python |
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from PIL import Image |
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import torch |
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from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration, BitsAndBytesConfig, TrainingArguments, Trainer |
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import time |
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model = PaliGemmaForConditionalGeneration.from_pretrained('NYUAD-ComNets/VehiclePaliGemma',torch_dtype=torch.bfloat16) |
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input_text ="extract the text from the image" |
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processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-pt-224") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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input_image = Image.open(image_path) |
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inputs = processor(text=input_text, images=input_image, padding="longest", do_convert_rgb=True, return_tensors="pt").to(device) |
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inputs = inputs.to(dtype=model.dtype) |
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with torch.no_grad(): |
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output = model.generate(**inputs, max_length=500) |
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result=processor.decode(output[0], skip_special_tokens=True)[len(input_text):].strip() |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- num_epochs: 5 |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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# BibTeX entry and citation info |
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``` |
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@misc{aldahoul2024advancingvehicleplaterecognition, |
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title={Advancing Vehicle Plate Recognition: Multitasking Visual Language Models with VehiclePaliGemma}, |
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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}, |
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year={2024}, |
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eprint={2412.14197}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2412.14197}, |
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} |
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