mmdetection3d
mmcv
| mmdetection3d | mmcv | |
|---|---|---|
| 3 | 4 | |
| 4,815 | 5,611 | |
| 2.2% | 1.2% | |
| 7.1 | 7.7 | |
| 13 days ago | 8 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mmdetection3d
-
What's the best model to get monocular 3d angle info
There are bunch of methods in this codebase, check it out. https://github.com/open-mmlab/mmdetection3d
-
MMDeploy: Deploy All the Algorithms of OpenMMLab
MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
-
Master thesis on autonomous vehicles (cybersecurity aspect)
You create and test the attacks on datasets like Kitti, NuScenes, and many others. Basically you try to manipulate the input to a certain detection pipeline for example (You can find a lot of LiDAR and camera based detection pipelines here: https://github.com/open-mmlab/mmdetection3d and here https://github.com/open-mmlab/mmdetection). You try to manipulate the input so that it deceives the car to do what you need without having control to the car itself.
mmcv
-
MMDeploy: Deploy All the Algorithms of OpenMMLab
MMCV: OpenMMLab foundational library for computer vision.
- Mmcv - Openmmlab computer vision foundation
-
An elegant and strong PyTorch Trainer
I opened source some works (AAAI 21 SeqNet, ICCV 21 MAED, etc) and earned more than 500 stars. After referring to some popular projects (detectron2, pytorch-image-models, and mmcv), based on my personal development experience, I developed a SIMPLE enough, GENERIC enough, and STRONG enough PyTorch Trainer: core-pytorch-utils, also named CPU. CPU covers most details in the process of training a deep neural network, including:
-
Why do practitioners still use regular tensorflow? [D]
Pretty much any custom layer, loss, ops, etc. For some of the most common ones used for objection detection, see here, examples include rotated iou/nms, deformable convolutions, focal loss variants, sync batch norm, etc.
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
3d-multi-resolution-rcnn - Official PyTorch implementaiton of the paper "3D Instance Segmentation Framework for Cerebral Microbleeds using 3D Multi-Resolution R-CNN."
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark
autogluon - Fast and Accurate ML in 3 Lines of Code
aiqc - End-to-end deep learning on your desktop or server.