Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias kecam
I couldn't find in the repo the means for conversion. Am I missing it here and if it's not available, could you share how these weights were converted.
I am also assuming that the provided training scripts do not provide for training the detector models. If I am mistaken, any guidance on this would be greatly appreciated. I am mistaken, it's clear in the README! sorry for the confusion. Nevertheless the above question of model conversion still stands.
Ya, the training script is coco_train_script.py. Just I still don't know the best parameters like optimizer / augment for training, so you need to find out by yourself. :)
You state in the YOLOR page (https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolor) that "Model weights converted from official publication." Similar statements are made for YOLOX and EfficientDet.
I couldn't find in the repo the means for conversion. Am I missing it here and if it's not available, could you share how these weights were converted.
I am also assuming that the provided training scripts do not provide for training the detector models. If I am mistaken, any guidance on this would be greatly appreciated.I am mistaken, it's clear in the README! sorry for the confusion. Nevertheless the above question of model conversion still stands.Thank you for making available this amazing repo.