Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs. Pull requests are welcome.
This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. All code and models are under active development, and are subject to modification or deletion without notice.
YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.
Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind
Releases(1.1.6)
1.1.6(Sep 8, 2022)
Add short form parameter
$ snd4onnx -h
usage:
snd4onnx [-h]
-rn REMOVE_NODE_NAMES [REMOVE_NODE_NAMES ...]
-if INPUT_ONNX_FILE_PATH
-of OUTPUT_ONNX_FILE_PATH
[-n]
optional arguments:
-h, --help
show this help message and exit.
-rn REMOVE_NODE_NAMES [REMOVE_NODE_NAMES ...], --remove_node_names REMOVE_NODE_NAMES [REMOVE_NODE_NAMES ...]
ONNX node name to be deleted.
-if INPUT_ONNX_FILE_PATH, --input_onnx_file_path INPUT_ONNX_FILE_PATH
Input onnx file path.
-of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
Output onnx file path.
-n, --non_verbose
Do not show all information logs. Only error logs are displayed.
YOLOv5-Lite:lighter, faster and easier to deploy Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, a
MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project developed by MMLab.
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).