mmdetection VS sahi

When I look for benchmarks I always start here https://paperswithcode.com/task/instance-segmentation/codeless it has the lists of datasets to measure models accross lots o papers. Many are very specific models with low support or community but it gives you a good idea of ​​the state of the art. It also lists repositories related to good community. https://github.com/open-mmlab/mmdetection seems very active and the one that is being used the most, you could use the models that it has integrated in its model zoo, within the same repository. It has the benchmarks to compare those same models and some of them are from 2022

MODEL: https://github.com/open-mmlab/mmdetection

Would it make sense to work with OpenMMLab (https://github.com/open-mmlab/mmdetection) or Pytorch-image-models (https://github.com/rwightman/pytorch-image-models#models) since they offer a variety of models?

I wanted to have a few choices getting localization into image search (index and search time). I immediately thought of using a region proposal network (rpn) from mask-rcnn to create patches that can also be indexed and searched (and add the localisation). I figured it might be somewhat agnostic to classes. I did not want to use mmdetection or detectron2 due to their dependencies and just getting the rpn was not worth it. I was encouraged by the PyTorch native implementations of detection/segmentation models but ended up finding yolox the best.

MMDetection: OpenMMLab detection toolbox and benchmark.

I have a semantic segmentation model trained using the mmdetection repo. Then it is converted to the ONNX format using the mmdeploy repo.

E.g. If you consider it an object detection problem it is: detect and localise all the pedestrians in a frame, and classify them by their (intended) action. IMO the easiest way to do this would be with mmdetection, which is built on top of pytorch. Just label your dataset, build a config, and boom you have a model. Inference with that model in only a few lines of code, you won't really need to learn too much to get started.

MMDetection

For example, in mmdetection, they expose options in their config & implementation to freeze batch norm layers in backbones and in this config, norm_eval is set to True meaning to freeze tracking of batch norm stats, while the ResNet backbone is frozen up to the 1st stage. Example of their backbone implementation can be found here.

MMDetection uses config Python scripting. It's easier to define nn.Module objects other than writing class name in a json config file

An alternative to this is to leverage existing object detection, apply the model to patches or slices of fixed size in our image, and then stitch the results together. This is the idea behind Slicing-Aided Hyper Inference!

Hi All, I am trying to detect defects in the images using YOLOv8where some of the classes (defectType1, defectType2) have very small bounding boxes and some of them have large bounding boxes associated with the, (defectType3, defectType4). Also, real-time operation is desired (at least 5Hz on Jetson Xavier) What I have done till now: I am primarily trying to use the SAHI technique (Slicing Aided Hyper Inference)

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?

I need to execute this model https://github.com/obss/sahi upon an HTTP request. I will need between 32GB and 128GB of RAM (depending on the request). Also, I will only receive this request once or twice a week (they are not predefined dates). Each process may take a few hours.

https://github.com/obss/sahi should do the job

I think you have multiple options: why run inference on this large resolution? Why not run on 1024x512 or smaller. Use a smaller model which uses less memory, eg enet, erfnet, bisenet etc. Otherwise, patchbased inference is the way to go, there is a nice library, but also easy to implement yourself: https://github.com/obss/sahi

i have the EXACT thing ! the libs github!

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).

[1] https://blog.roboflow.com/detect-small-objects/

[2] https://github.com/obss/sahi

I am not very experienced, but do I understand that the problem is the size of the image? If so, have you heard of sahi

Try slicing and yolov4. https://github.com/obss/sahi