susanin1970

No, I didn't solve it yet, but I'm going to solve in the future Now I try to use Detectron2 library for this task

@InvincibleKnight, hi! Detectron2 library was worked for me, but there were some nuances I use config `COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml` for training I changed this config in the following way: ``` cfg =...

@tehkillerbee , hi! How many objects contain in the frames of your dataset?

> > @InvincibleKnight, hi! > > Detectron2 library was worked for me, but there were some nuances > > I use config `COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml` for training > > I changed this...

> @susanin1970 Sorry for late reply, I detect around 1000 in my dataset. Each frame is scaled to 768x768. > > The inference speed depends on the number of objects...

Assuming that the mAP in YOLACT is measured using pycocotools, i.e. using the mAP from the MS COCO competition, the definitions of the AP metrics for small, medium and large...

> i know that yolov5 support Dynamic size input That's right, but I dont think, that FastestDet is based on YOLOv5

Yes, I checked SRN model after converting to the inference formar with use paddle I use `predict_rec.py` script in PaddleOCR repository for getting predictions: ``` python .\tools\infer\predict_rec.py --rec_model_dir=D:\Repositories\PaddleOCR\output\rec\srn\inference_model --rec_algorithm=SRN --rec_image_shape="1,64,256"...

Link on my SRN inference model: https://drive.google.com/file/d/1nYDrG9zDO6Sjo7oYvN57mmmwy3jNzGL7/view?usp=sharing

Yes, paddle inference model has multiple inputs: