Releases: dnum-mi/basegun-ml
Releases · dnum-mi/basegun-ml
basegun-ml v2.0.0
Changelog:
Add the OCR module with alarm model detection
Add exception management when a card or a firearm is absent in the measure module
Change the classification model from nano to small
Deep Learning Model version
Classification model
The classification model is based on YOLOV8.
model size : Small
dataset used : basegun V1
data augmentation : flip LR : 0.2, flip UD: 0.2, rotate +-180
hyperparameters : adamW, batch size 16
training date : January 15th 2024
Keypoint detection model
The keypoint detection model is based on YOLOV8.
model size : nano
dataset used : keypoints V1
data augmentation : None to avoid creation of "false" images
hyperparameters : Loss function : euclidean distance instead of OKS, Optimizer AdamW
training date : October 27th 2023
Oriented Bounding Box card detection model
The Oriented Bounding Box card detection model is based on YOLOV5.
model size : nano
dataset used : Card detection V1
data augmentation : No flip because of bounding box order, HSV augmentation full, rotate +-180
hyperparameters : batch size 2, optimizer: Adam
training date : December 4th 2023
OCR models
The OCR model is based on paddleOCR
recognition and detection models: ch_PP-OCRv4
classification mode ch_ppocr_mobile_v2.0_cls_infer
IQA model
the IQA model is based on CNNIQA
basegun-ml v1.0.1
First version of the basegun-ml package
Deep Learning Model version
Classification model
The classification model is based on YOLOV8.
model size : nano
dataset used : basegun V1
data augmentation : flip LR : 0.2, flip UD: 0.2, rotate +-180
hyperparameters : adamW, batch size 16
training date : January 15th 2024
Keypoint detection model
The keypoint detection model is based on YOLOV8.
model size : nano
dataset used : keypoints V1
data augmentation : None to avoid creation of "false" images
hyperparameters : Loss function : euclidean distance instead of OKS, Optimizer AdamW
training date : October 27th 2023
Oriented Bounding Box card detection model
The Oriented Bounding Box card detection model is based on YOLOV5.
model size : nano
dataset used : Card detection V1
data augmentation : No flip because of bounding box order, HSV augmentation full, rotate +-180
hyperparameters : batch size 2, optimizer: Adam
training date : December 4th 2023
Dataset v0
- add functions for testing images in post_training.py
- regorganize models folder
- write more details about training params
- keep lr in checkpoints
- do not shuffle val dataset