[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
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Updated
Oct 3, 2023 - Python
[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
[CVPR 2024 Highlight] DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
volksdep is an open-source toolbox for deploying and accelerating PyTorch, ONNX and TensorFlow models with TensorRT.
[NeurIPS 2022] Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Pytorch implementation of our paper accepted by CVPR 2020 (Oral) -- HRank: Filter Pruning using High-Rank Feature Map
Benchmarking suite to evaluate 🤖 robotics computing performance. Vendor-neutral. ⚪Grey-box and ⚫Black-box approaches.
TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
A package for processing signals recorded using wearable sensors, such as Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activity (EDA) and 3-axis acceleration (ACC).
动手学习TVM核心原理教程
Implementation of "Fully Learnable Group Convolution for Acceleration of Deep Neural Networks", CVPR'19
Pytorch implementation of our paper accepted by CVPR 2022 -- IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
Caffe Computation Graph Optimization.
Pytorch implementation of our paper accepted by ECCV2022 -- Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
PyTorch implementation of "Dynamic Structure Pruning for Compressing CNNs" (AAAI 2023 Oral)
pymotiontracker is a Python 3 library to read from an MPU6050 (accelerometer + gyroscope) Bluetooth module
CircuitPython I2C driver for MPU9250 9-axis motion tracking device
Riverbed Community Toolkit is a public toolkit for Riverbed Solutions engineering and integration
Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization
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