TACO
Swin-Transformer-Object-Detection
| TACO | Swin-Transformer-Object-Detection | |
|---|---|---|
| 3 | 4 | |
| 557 | 1,710 | |
| - | 0.1% | |
| 0.0 | 0.0 | |
| about 1 year ago | about 1 year ago | |
| Jupyter Notebook | Python | |
| MIT License | 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.
TACO
-
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
-
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.
-
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.
Swin-Transformer-Object-Detection
-
Transfer Learning on Swin Transformer as a backbone for instance segmentation using MRCNN
I'm currently trying to transfer learn a set of custom classes of fish, for instance segmentation. I have found the official implementation of Swin Transformer as a backbone for instance segmentation using MRCNN: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection.
-
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.
-
[P] I implemented DeepMind's "Perceiver" in PyTorch
Yes, have a look at this paper.
-
[P] Code and pretrained models for Swin Transformer are released (SOTA models on COCO and ADE20K)
Object detection on COCO: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection
What are some alternatives?
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/
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
Video-Swin-Transformer - This is an official implementation for "Video Swin Transformers".
TrainYourOwnYOLO - Train a state-of-the-art yolov3 object detector from scratch!
Swin-Transformer-Tensorflow - Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)
revery - :zap: Native, high-performance, cross-platform desktop apps - built with Reason!
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
theme-ui - Build consistent, themeable React apps based on constraint-based design principles
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow