Pinned Repositories
copy-paste-aug
Copy-paste augmentation for segmentation and detection tasks
Decoder_hack_EVRAZ
fuzzy-doc-search
OCR scanned pdf and fuzzy string search in xlsx/pdf
gbr-yolov5-metric
add f2-score to yolov5 model
leafmeter
min_prirodi_soyuz
pleural_effusion_segmentation
R-CenterNet
detector for rotated-object based on CenterNet/基于CenterNet的旋转目标检测
reef-solution
reef-solution for upload
this-fruit-does-not-exist
JI411's Repositories
JI411/fuzzy-doc-search
OCR scanned pdf and fuzzy string search in xlsx/pdf
JI411/copy-paste-aug
Copy-paste augmentation for segmentation and detection tasks
JI411/Decoder_hack_EVRAZ
JI411/gbr-yolov5-metric
add f2-score to yolov5 model
JI411/leafmeter
JI411/min_prirodi_soyuz
JI411/pleural_effusion_segmentation
JI411/R-CenterNet
detector for rotated-object based on CenterNet/基于CenterNet的旋转目标检测
JI411/reef-solution
reef-solution for upload
JI411/this-fruit-does-not-exist
JI411/retinanet-examples
Fast and accurate object detection with end-to-end GPU optimization
JI411/rotation-yolov5
rotation detection based on yolov5
JI411/sahi
A lightweight vision library for performing large scale object detection/ instance segmentation.
JI411/speech_analytics
Speech analytics package for call-center
JI411/svoice
We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
JI411/tiger_or_leopard
JI411/todo.md
TODO.md file format - todomd.org
JI411/yolact
A simple, fully convolutional model for real-time instance segmentation.
JI411/yolo-tiling
Tile (Slice) YOLO Dataset for Small Objects Detection
JI411/yolor
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
JI411/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
JI411/yolov7-face
yolov7 face detection with landmark