Cartridge Case Images Segmentation

This repo provides segmentation model for Cartridge Case part segmentation tasks with Segformer, it will segment the images into {"0": "unlabeled", "1": "breech-face", "2": "aperture-shear", "3": "firing-pin-impression", "4": "firing-pin-drag", "5": "other"}. The trained model is provided in checkpoints folder.

Getting Started

This repo is tested with Conda environment and Python 3.9 under Linux os, please run below command to install dependencies

pip install -r requirements.txt

Data Annotation

Segments.ai is used to annotate the images, before annotating the images, the data are converted into grayscale to simulate 3D images with convert_color.py

Transfer Learning

Firstly we used train_hf.py for fine-tuning the model and save the fine-tuned model in local. The pretrained model we used is nvidia/mit-b0. Then we use train.py to train the fine-tuned model with image augmentations to further improve model performance.

Model Training

Please use train.py to train the model, modify the below arguments before training

args = Params(
        hf_dataset_identifier = "issacchan26/gray_bullet", 
        pretrained_model_name = '/path to pretrained model folder from Hugging Face', # path to pretrained model
        epochs = 100,
        lr = 0.0005,
        batch_size = 1,
        checkpoints_path = "/path to/checkpoints/"  # path to checkpoints saving folder
        )

Inference

Please use inference.py to infer the images, put all the images you would like to infer inside a folder (below we use 'infer_query' as folder name). Modify below path before running:

pretrained_model_name = '/path to/checkpoints/best',  # path to model folder
prediction_save_path = '/path to/prediction/', # path to saving folder
infer_folder = '/path to/infer_query/' # path to the image folder to be inferred

To reproduce the validation results

1. test.py
   It is used to reproduce the validation results of our fine-tuned model
2. infer_hf_ds.py
   It is used to infer the dataset from Hugging Face

Please modify the below path before running

pretrained_model_name = '/path to/checkpoints/best',  # path to model folder
prediction_save_path = '/path to/prediction/', # path to saving folder