Zhuoyang Zhang

Hi MenSanYan, To perform TensorRT inference on multiple boxes, you can run the following command: `python deployment/sam/tensorrt/inference.py --model xl1 --encoder_engine assets/export_models/sam/tensorrt/xl1_encoder.engine --decoder_engine assets/export_models/sam/tensorrt/xl1_decoder.engine --img_path assets/fig/my_example.jpg --mode boxes --boxes "[[x1,y1,x2,y2],[x3,y3,x4,y4]]"` Best,...

Hi @Dongshengjiang, Thanks for your attention. We have released the training code. Best, Zhuoyang

Hi ghm666, It determines the minimum and maximum number of points/boxes that the TensorRT engine can accept. A single point's coordinate is formatted as 1x1x2, and its label is formatted...

Hi SoulProficiency, We recommend you to use the latest TensorRT version 8.6. Best, Zhuoyang

Hi asd841018 and zqd-big, TensorRT tries various optimization tactics during the build phase. It looks like there is a tactic that tries to use more memory than the Jetson AGX...

Hi @aniket03 and @asd841018, Thank you for raising this issue and trying to solve it. I have made the necessary updates to the code to address the issue. You can...

Hi pvtoan, We use `predict` function of the predictor in the demo file which supports single bounding box input. You can instead use `predict_torch` function which supports multiple bounding boxes...