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Train an RL agent to execute natural language instructions in a 3D Environment (PyTorch)
The main features of devendrachaplot/deeprl-grounding are: Computer Vision Models, Language Grounding in Vision, Model Implementations.
Projects with overlapping indexed features include: amdegroot/ssd.pytorch — This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and… aosokin/biogans — Implementation of "GANs for Biological Image Synthesis". aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,… 1zb/deformable-convolution-pytorch — PyTorch implementation of Deformable Convolution. 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… bamos/densenet.pytorch — A PyTorch implementation of DenseNet.
PyTorch implementation of Deformable Convolution
This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra