Learn Data Science using Python

  Another post starts with you beautiful people! It is overwhelming for me to see massive interest in my last three posts about the YOLOv7 series💓. Your response keeps me motivated to share my learning with you all 💝. If you have not checked my previous posts about YOLOv7, then I am sharing here links to read those once and then proceed with this post- Train a custom dataset with YOLOv7 Export custom YOLOv7 model to ONNX Export custom YOLOv7 model to TensorRT Till now we have learned about object detection with YOLOv7. In this post, we are going to learn how can we train a custom dataset for instance segmentation task with YOLOv7 👌. For your information instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image. For our hands-on we need a dataset having images and their annotations in polygon format and of course in YOLO format. So I have found and downloaded the American Sign Language dataset in the required format from  th

  Another post starts with you beautiful people! Thanks for the great response to my  last post  where we successfully exported a custom-trained YOLOv7 model to ONNX format.  In this post, we are going to move one step further to achieve high-performance inference using  NVIDIA TensorRT . We will learn how can we export our YOLOv7 ONNX model to a TensorRT format. NVIDIA TensorRT is an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications. This file format is widely used in case you need to deploy your model into edge devices but with high inference speed. So knowledge of exporting your YOLOv7 model to TensorRT format is very beneficial learning. Please note the exportation of the model is environmentally sensitive. It is recommended to use the same environment where you have trained the original YOLOv7 model. For this post as well I am using the same Google Cola

  Another post starts with you beautiful people💓. In my previous post, we learned about training a custom dataset with an official Pytorch-based YOLOv7 object detector. If you have not seen that post, I recommend you check it once. The link is  here . Once we achieve the best model, the next important step is to use that model. Sometimes you may need to use multiple ML models which you have trained on different-different ML frameworks like PyTorch, TensorFlow, Caffe, etc. In production or the real world, the trained model can be deployed as Rest API, or integrated with a web application without changing its form but what if you need to use that within your mobile device as an Android or iOS app as well as you want to use it with an embedded system like Nvidia Jetson 💣? Here comes the problem of interoperability. In this post, we are going to learn how can we export our custom YOLOv7 model to ONNX format. ONNX  is an open format built to represent machine learning models. ONNX defines