Background When using Traditional CNN-backboned architecture models, due to the convolutional filter’s weights being fully fixed after training, they suffered to adapt to different inputs dynamically. Vision Transformers attempted to remove the convolution from the backbone, but since it is…
Compared with 2D Object Detection, there are several additional parameters to consider when it comes to 3D object detection 2D Object Detection: X coordinate for the center of the bounding box Y coordinate for the center of the bounding box…
I’d like to share 2 different ways to save and load a model using Pytorch. Saving The Entire Model #save model torch.save(model, PATH) #load model model = torch.load(PATH) model.eval() This save/load process has the least amount of code to implement.…
These are my top3 books that are helping me learn deep learning! 1. Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio 2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems…
Polynomial Learning Rate For deep learning models, the learning rate is one of the most important hyper-parameters in any deep neural network optimization process. Polynomial Learning Rate is a proposed technique to apply learning rate decay and optimize such process.…
Today I tried to do transfer learning using PIDNet (Since I just learned about PIDNet). Compared to my first attempt, the output is getting slightly better but still not to the level where it is actually useful.
Today I’ve learned about bilinear interpolation for images, so I’d like to share it here. To simplify the concept, here is an example if we were to upscale an image by a factor of 2 using bilinear interpolation. For semantic…
PIDNet Today I’ve learned about PIDNet, so I’d like to share it here. Previously, I learned about BiSeNet which had a two-branched architecture to solve high latency problems. However, this architecture suffers another problem called “overshoot” where the boundary of…
torch.nn.CrossEntropyLoss() Using torch.nn.CrossEntropyLoss() as a loss function for semantic segmentation models was first confusing for me, so I’d like to share it here. CrossEntropyLoss is for multi-class models and it expects at least 2 arguments. One for the model prediction…
Today I’ve learned about Bagging, so I’d like to share it here. Bagging is when you have multiple models to vote for the correct answer. This helps decrease generalization errors.