Open curtis18 opened 1 week ago
Thank you for your tutorial. I would like to ask can the output of boxes and masks from ONNX model be the same as the output of PyTorch model? So that a classs can be created to support both ONNX and PyTorch model output?
# YOLO results = self.model.predict(source=image, device=device) boxes = results[0].boxes.data.cpu().numpy() masks = results[0].masks.data.cpu().numpy() if results[0].masks else None
# ONNX outputs = self.model.run(None, {input_name: image_np}) boxes, masks = self.model_onnx_seg_convert(outputs) # a new function
Thank you for your tutorial. I would like to ask can the output of boxes and masks from ONNX model be the same as the output of PyTorch model? So that a classs can be created to support both ONNX and PyTorch model output?