Showing 1-16 of 16 projects
A Python library that empowers developers to build applications and systems with self-contained computer vision capabilities.
Segmentation models with pretrained backbones for image processing and computer vision tasks.
A PyTorch implementation of the DenseNet architecture, a state-of-the-art convolutional neural network.
A PyTorch repository for practicing image classification on the CIFAR-100 dataset using various deep learning models.
A Chinese OCR (Optical Character Recognition) library built using CTPN, DenseNet, and CTC.
A suite of tools for implementing, training, and testing semantic segmentation models in TensorFlow.
A PyTorch-based deep learning framework for 2D/3D medical image segmentation.
PyTorch-based library for image classification tasks like CIFAR-10, CIFAR-100, and ImageNet.
A memory-efficient implementation of the DenseNet deep learning architecture in PyTorch.
A comprehensive PyTorch-based code repository for image classification, including training, prediction, TTA, model fusion, deployment, and more.
An experimental OCR project implementing a CNN+BLSTM+CTC architecture for vision-based text recognition.
A deep learning model for removing watermarks and overlays from images, useful for vibe coders working with AI-generated content.
Classification models trained on ImageNet, for use in computer vision and image classification tasks.
A template for deploying a Keras-based image classifier web app using Flask in 10 minutes.
A deep learning library for EEG tasks classification, built with TensorFlow.
High-level network definitions with pre-trained weights in TensorFlow for deep learning and computer vision tasks.
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