platerec-model is a model for recognizing text from images, specifically designed for license plate recognition. The project utilizes a neural network architecture with an encoder-decoder setup and uses SAM (Sharpness-Aware Minimization) for optimizing the model training process. It's really lightweight using only a mobilenet v2 for encoder and a decoder transformer (gpt) for decoder. It is used in the platerec project.
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Clone the Repository:
git clone https://github.com/your-username/platerec-model.git cd platerec-model -
Install Dependencies:
pip install -r requirements.txt
To train the model, use the following command:
python train.py --dataset_paths dataParameters:
--dataset_paths: A list of directories containing the input data files. The directory should contain the images and txt files, for example: 1.jpg and a 1.txt with the plate text, like:AWD1E33in plain text.
Example:
├── 1.jpg
├── 1.txt
├── 2.jpg
├── 2.txt
├── 3.jpg
├── 3.txt
├── 4.jpg
└── 4.txt
--model_checkpoint: Path to a pretrained model (.pth file) if you have.--device: The device to use for training (cudaorcpu). Defaults tocudaif available.--num_epochs: Number of epochs for training. Default is 10.
To perform inference with the trained model, use the following command:
python inference.py --model_path artifacts/trained_model.pth --image_path lp_cropped.jpgParameters:
--model_path: Path to the trained model checkpoint (.pth file).--image_path: Path to the image file for which text recognition is to be performed.
The platerec-model employs an encoder-decoder architecture with cross-attention mechanisms. The key components are:
- Encoder: Based on
mobilenet_v2for feature extraction from images. - Decoder: Utilizes an embedding layer, position encoding, and multiple decoder blocks with self-attention and cross-attention layers.
- Loss Function: Uses
cross_entropyloss, with special handling for a specific index (ignore_index=39).