Search Results for "torch"

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865.

...This innovation lets the model learn domain-to-domain translations like turning horses into zebras, changing seasons, or transforming photos into paintings, using only collections of images from each domain. The original implementation (in Torch) has since been complemented by other re-implementations (including in PyTorch), but the core idea remains: unpaired image-to-image translation. Because of its flexibility, CycleGAN has become one of the most widely adopted generative models for domain translation tasks.

...The design is modular and homogeneous, making it relatively easy to scale (by tuning cardinality, width, depth) and adopt in existing residual frameworks. The official repository offers a Torch (Lua) implementation with code for training, evaluation, and pretrained models on ImageNet. In practice, ResNeXt models often outperform standard ResNet models of comparable complexity.

MultiPathNet is a Torch-7 implementation of the “A MultiPath Network for Object Detection” paper (BMVC 2016), developed by Facebook AI Research. It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline.

...A companion refinement model (SharpMask) sharpens the coarse predictions, recovering fine boundaries like thin limbs or object edges. The repository (in the original Torch/Lua stack) includes pretrained weights, training scripts, and evaluation utilities.

The torch-rnn project is a lightweight and efficient implementation of recurrent neural networks built on the Torch framework, focusing on flexibility and reusability for sequence modeling tasks. It provides implementations of standard RNNs and long short-term memory networks, enabling users to train models for tasks such as text generation, language modeling, and sequence prediction.