yolov3-tf2
TACO
| yolov3-tf2 | TACO | |
|---|---|---|
| 3 | 3 | |
| 2,510 | 540 | |
| - | - | |
| 2.4 | 0.0 | |
| 7 days ago | about 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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yolov3-tf2
TACO
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Does a high tech Trash can 🗑 that sorts out plastic and trash out by scanning exist?
http://tacodataset.org/ <- Open source dataset if you want to train a classifier, I like this one
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Advice on Masters project | Vision transformers
Hi, So my project is to do with object detection on trash in the wild on this fairly obscure dataset: http://tacodataset.org/ and I was thinking of applying vision transformers to it for feature extraction. I was thinking of taking the YOLOX implementation and swapping out the backbone with swin transformers and perform bunch of comparisons/experiments for the write up. Sort of like how they applied swin transformers to mask R-CNN here but I am struggling to understand where to begin.
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How to convert Polygons to Bounding Boxes?
I was wondering if anyone had a script or could point me to one that would be able to convert polygons from image segmentation to bounding boxes for object detection. I am looking to create a trash detector to run on my trash picking up robot. I found the TACO dataset, but it uses segmentation and I just want to start with bounding boxes. Any help would be appreciated.
What are some alternatives?
yolact - A simple, fully convolutional model for real-time instance segmentation.
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
tensorflow-yolo-v3 - Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
yolact - Tensorflow 2.x implementation YOLACT
TrainYourOwnYOLO - Train a state-of-the-art yolov3 object detector from scratch!
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
revery - :zap: Native, high-performance, cross-platform desktop apps - built with Reason!
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
theme-ui - Build consistent, themeable React apps based on constraint-based design principles
tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.