fashionpedia-api
Yet-Another-EfficientDet-Pytorch
| fashionpedia-api | Yet-Another-EfficientDet-Pytorch | |
|---|---|---|
| 2 | 1 | |
| 152 | 5,183 | |
| - | - | |
| 0.0 | 0.0 | |
| 6 months ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 2-clause "Simplified" License | GNU Lesser General Public License v3.0 only |
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fashionpedia-api
Yet-Another-EfficientDet-Pytorch
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Bounding box annotations and object orientation
However, there are papers on oriented object detectors (see https://arxiv.org/pdf/1911.07732.pdf) for example. In that paper, they do achieve better results using oriented bounding boxes. If you want to go down that route, I would suggest using the EfficientDet model, because the PyTorch code that you'll find for it is quite easy to understand and modify. For example, I've taken https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch, and modified it to include a "thing-ness" logit, and this was pretty easy to do. Classic EfficientDet models only include logits (aka output neurons that get softmax-ed) for each class, and if any one of these class neurons is greater than 0.5, then it is considered "a thing". Anyway - that's digression, but my point is that I've thought about adding oriented box support to an EfficientDet model, and it didn't seem to be too hard, although I haven't actually done it. If I was to start now, I would probably go with https://github.com/rwightman/efficientdet-pytorch, since Ross Wightman's models are becoming a de-facto standard in the PyTorch world for all things image-related.
What are some alternatives?
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
LLVIP - LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
TFLiteDetection - TensorFlow Lite Object Detection Python Implementation
cvdv - Computer Vision Data Visualization
SipMask - SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
mobilenets-ssd-pytorch - MobileNets-SSD/SSDLite on VOC/BDD100K Datasets