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I need that the model still detects the 80 classes of coco, but i have extra images (labeled) that are different and not included in the coco dataset but they. Please note that there will not be a coco 2021 challenge, instead, we encourage people to participate in the lvis 2021 challenge. I am using yolo v8 ultrlytics, pretrained weights on coco dataset
It is trained on 80 classes We are pleased to announce the lvis 2021 challenge and workshop to be held at iccv Now i want to add some more classes in my trained model, without losing previous one
For example i have 4 new classes
The difficulty arises from the fact that weights in a deep learning model are not specific to any class such that you can freeze some specific weights and thereby freeze the performance of the model on specific classes, and then train others or add newer ones. Follow their code on github. Confidences not normalized (frigate/openvino integration issue) #2138 · kerotonic opened on sep 18 This is code release for our paper incremental learning of object detectors without catastrophic forgetting published on iccv 2017
Code is written for python 3.5 and tensorflow 1.5 (might require minor modifications for more recent versions) You are also expected to have normal scientific stack installed Now the new weights (best.pt or last.pt) are detecting class b only on the given test images.
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