PixelLib
sahi
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PixelLib | sahi | |
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3 | 10 | |
1,008 | 3,534 | |
- | 3.4% | |
0.0 | 6.6 | |
6 months ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
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PixelLib
sahi
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Changing labels of default YOLOv5 model
I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?
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Roboflow 100: A New Object Detection Benchmark
Good idea. I haven’t looked too closely yet at the “hard” datasets.
We originally considered “fixing” the labels on these datasets by hand, but ultimately decided that label error is one of the challenges “real world” datasets have that models should work to become more robust against. There is some selection bias in that we did make sure that the datasets we chose passed the eye test (in other words, it looked like the user spent a considerable amount of time annotating & a sample of the images looked like they labeled some object of interest).
For aerial images in particular my guess would be that these models suffer from the “small object problem”[1] where the subjects are tiny compared to the size of the image. Trying a sliding window based approach like SAHI[2] on them would probably produce much better results (at the expense of much lower inference speed).
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Which model is best for detecting small objects? Yolov3? MaskRCNN, Faster-RCNN?
Try slicing and yolov4. https://github.com/obss/sahi
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
FasterRCNN - Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras.
fashion-segmentation - A tensorflow model for segmentation of fashion items out of multiple product images
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
awesome-tiny-object-detection - 🕶 A curated list of Tiny Object Detection papers and related resources.
fastdup - fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.
datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
PaddleSeg - Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.