In the case of an error involving new versions of libraries not being backwards compatible, I saved the conda environment with all of the versions that were used to create the first 2000 models seen in utils/2000networks.csv in utils/working_conda_env.yml. To recreate the environment w...
Breadcrumbs Tiny-Imagenet-200 / README.mdTop File metadata and controls Preview Code Blame 181 lines (129 loc) · 5.95 KB Raw Tiny-Imagenet-200 This repository is my personal research code for exploration of Convolutional Neural Networks, specifically on the Tiny-Imagenet-200 dataset. I plan...
Keras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 Xception Let’s start with a overview of the ImageNet dataset and then move into a brief discussion of each network architecture. What is...
To apply the Keras models pre-trained on the ImageNet dataset to your own images, make sure you use the“Downloads”form at the bottom of this blog post to download the source code and example images. This will ensure your code is properly formatted (without errors) and your directory str...
Demonstrating competitiveness, ERNs achieve an ImageNet top-1 accuracy of 72.5pt with a ResNet50-compatible architecture and 63.6pt with a model size less than 1MB. Moreover, ERNs exhibit impressive inference times, reaching 300FPS with the smallest model and 60FPS with the largest model on a...
cuDNN (compatible with your installed CUDA Toolkit) Usage Below is an example of a simple model based on KAN convolutions: import torch import torch.nn as nn from kan_convs import KANConv2DLayer class SimpleConvKAN(nn.Module): def __init__( self, layer_sizes, num_classes: int = 10,...
I collected training dataset images and fine awesome tool for labeling images. But it generates xml files. So I needed to implement tool which translates from ImageNet xml format to Darknet text format. Also compatible with latest YOLOv5 by Ultralytics....
cuDNN (compatible with your installed CUDA Toolkit) Usage Below is an example of a simple model based on KAN convolutions: import torch import torch.nn as nn from kan_convs import KANConv2DLayer class SimpleConvKAN(nn.Module): def __init__( self, layer_sizes, num_classes: int = 10,...