Here is one way to visualize your model’s training process using tensorboard with Pytorch #Inside Code for Training from torch.utils.tensorboard import SummaryWriter() tb = SummaryWriter() #Add Metrics you want to evaluate tb.add_scalar(‘Loss’,loss,epoch) tb.add_scalar(‘Accuracy’,accuracy,epoch) tb.add_scalar(‘F1Score’,fa_score,epoch) To start a tensorboard server go…
I did an experiment to compare how model performance change when converted to TensorRT engine. These are the steps I took. 1. Train Several Binary Segmentation Model 2. Randomly Choose 30 images from the dataset 3. Run inference with the…
Argmax compares pixels in the same position across channels, and acquires the index of the highest channel. This can be useful for semantic segmentation. Semantic segmentation models outputs the same width and height as the input image and creates a…
There are several ways to convert arrays to torch tensors. import torch import numpy as np data = np.array([1,2,3,4,5,6]) option1 = torch.Tensor(arr) option2 = torch.tensor(arr) option3 = torch.as_tensor(arr) option4 = torch.from_numpy(arr) Option Description 1 Make a Copy and convert array…
I found out why my TensorRT engine model was not working as expected. I messed up with configuring the preprocessing step for Deepstream. When you use Deepstream to run inference, there is a property called net-scale-factor and offsets which you…
I’ve compared the performance between the model before and after converting to TensorRT engine. The output from the TensorRT engine is flattened to a 1d array, so I had to convert that back to a 2d array to display the…
A semantic segmentation model outputs a tensor shaped [Batch_size,Channel(Number of Classes),Img_Height,Img_Width] (If using Pytorch), but if you convert that to a TensorRT engine for faster inference, the output is flattened to a 1d array. Therefore the shaping being, [(Batch_size)X(Channel)X(Img_Height)X(Img_Width),] Considering…
Unet++ is useful when you want to improve image segmentation accuracy. This was first designed for medical use where accuracies are critical. In a nutshell, Unet++ adds convolution layers between skip connection. The original Unet skip connect without any additional…
This is how an AI learns how to classify my dog. I’ve found a blog post visualizing the feature maps of a classification model, so I tried it out! I’m going to use Resnet18 and visualize the feature maps for…
I’ve quantitatively evaluated binary segmentation models using IOU as my metric. The reason why I used IoU is because IoU penalizes a single instance of classification error more compared to other metrics such as f1 scores, therefore lowering the value.…