mmdetection3d
mmselfsup
| mmdetection3d | mmselfsup | |
|---|---|---|
| 3 | 5 | |
| 4,815 | 3,084 | |
| 2.2% | 0.7% | |
| 7.1 | 5.3 | |
| 13 days ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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mmdetection3d
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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
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
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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.
mmselfsup
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
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Does anyone know how a loss curve like this can happen? Details in comments
For some reason, the loss goes up shaply right at the start and slowly goes back down. I am self-supervised pretraining an image modeling with DenseCL using mmselfsup (https://github.com/open-mmlab/mmselfsup). This shape happened on the Coco-2017 dataset and my custom dataset. As you can see, it happens consistently for different runs. How could the loss increase so sharply and is it indicative of an issue with the training? The loss peaks before the first epoch is finished. Unfortunately, the library does not support validation.
- Defect Detection using RPI
- [D] State-of-the-Art for Self-Supervised (Pre-)Training of CNN architectures (e.g. ResNet)?
- Rebirth! OpenSelfSup is upgraded to MMSelfSup
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
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.
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
3d-multi-resolution-rcnn - Official PyTorch implementaiton of the paper "3D Instance Segmentation Framework for Cerebral Microbleeds using 3D Multi-Resolution R-CNN."
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
barlowtwins - Implementation of Barlow Twins paper
autogluon - Fast and Accurate ML in 3 Lines of Code
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]