model-checkpoint

There are 0 repository under model-checkpoint topic.

angelolamonaca / PyTorch-Precision-Converter
A flexible utility for converting tensor precision in PyTorch models and safetensors files, enabling efficient deployment across various platforms.
bf16 deep-learning deployment fp16 machine-learning model-checkpoint model-compression model-conversion optimization pytorch toolkit utilities efficient-deployment tensor-precision
Language:Python 6
caailab / features_injection
Features injected recurrent neural networks for short-term traffic speed prediction
intelligent-transportation-systems time-series-analysis deep-learning traffic-speed-forecasting traffic-state-estimation contextual-factors feature-fusion keras scikit-learning early-stopping model-checkpoint
Language:Python 2
renan-siqueira / autoencoder-research
A comprehensive toolkit built with PyTorch, designed to facilitate the training, evaluation, and visualization of autoencoders. From simple linear autoencoders to convolutional and variational architectures, this project offers an intuitive and expandable framework for anyone delving into the realm of unsupervised learning.
autoencoder deep-learning machine-learning python pytorch computer-vision convolutional-neural-network data-augmentation dimensionality-reduction image-processing image-reconstruction model-checkpoint neural-networks unsupervised-learning variational-autoencoder container docker dockerfile virtualization
Language:Python 2
gulabpatel / Model_Checkpoingting
checkpoint checkpointing model-checkpoint
Language:Jupyter Notebook 1
myselfHimanshu / ultron-vision
Computer Vision State Of The Art Intuition Project in Pytorch; WIP
albumentations cifar10 cnn convolutions deep-learning densenet gradcam kernels lrfinder machine-learning mnist model-checkpoint onecyclepolicy python3 pytorch regularization resnet scratch tinyimagenet transformations
Language:Jupyter Notebook 1
ishreya09 / Skin-Cancer-Detection
Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.
batch-normalization cnn-classification early-stopping fcnn gradcam-heatmap l2-regularization model-checkpoint dropout machine-learning
Language:Jupyter Notebook