yolact
edgetpu-yolo
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| yolact | edgetpu-yolo | |
|---|---|---|
| 4 | 2 | |
| 4,924 | 81 | |
| - | - | |
| 0.0 | 2.6 | |
| 6 months ago | 9 days ago | |
| Python | Python | |
| MIT License | - |
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yolact
- YOLOv6: Redefine state-of-the-art for object detection
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Instance segmentation
YOLACT
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Advice needed
The Github repo is not highly structured and is probably written during the time of the author's research. Been using this for well over a year and does the job so well.
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A question on Instance Segmentation Labelling
I am using yolact : https://github.com/dbolya/yolact
edgetpu-yolo
- YOLOv6: Redefine state-of-the-art for object detection
-
A microcontroller board with a camera, mic, and Coral Edge TPU
I'm on the fence. It's a very nice device if you can get your models working on it - basically untouched at the price/power point. Drivers for me have been OK. I have an M.2 card connected to a Jetson devkit (makes for a nice embedded test bench) and it runs fine, no worse than the NCS for setup anyway. There were a couple of PCI settings to tweak but I documented the setup here [0]. For common use cases it's a decent option, I think. For custom models you really need to know what you're doing.
The main issue I've had is that the compiler behaviour differs between versions (and it's very difficult to find older releases), so where previously you could run a big model and delegate things to the CPU, now it sometimes won't compile at all. There were also problems where we trained a model in AutoML - using free credits but the real cost would have been over $100 - but edgetpu compiled model lost a lot of performance. The developers have been very helpful when I've contacted them, and generally you can get through to real devs (not generic support) who can look at your model for you. Mostly I think you need to take care when training models for these devices, but quantisation-aware training is not trivial to use in Tensorflow and there are only a few off-the-shelf models which are supported in the various toolkits. Model maker looks promising, but it's also finnicky in my experience [1].
I'm not super worried about hardware availability. They're suffering from the chip shortage like everyone else, so it's not surprising that lead times are long. I was able to buy my device in late 2020 without any trouble.
[0] https://github.com/jveitchmichaelis/edgetpu-yolo/blob/main/h...
What are some alternatives?
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
yolact_edge - The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
frigate - NVR with realtime local object detection for IP cameras
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
yolov7_d2 - 🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
mmdetection - OpenMMLab Detection Toolbox and Benchmark
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/