efficient-model
There are 36 repositories under efficient-model topic.
mit-han-lab/temporal-shift-module
[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
mit-han-lab/once-for-all
[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
mit-han-lab/proxylessnas
[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
mit-han-lab/amc
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
mit-han-lab/haq
[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision
microsoft/nn-Meter
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.
SqueezeAILab/KVQuant
[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
mit-han-lab/hardware-aware-transformers
[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
amirgholami/ZeroQ
[CVPR'20] ZeroQ: A Novel Zero Shot Quantization Framework
kssteven418/I-BERT
[ICML'21 Oral] I-BERT: Integer-only BERT Quantization
mit-han-lab/amc-models
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
youngwanLEE/VoV3D
Efficient 3D Backbone Network for Temporal Modeling
d-li14/HBONet
[ICCV 2019] Harmonious Bottleneck on Two Orthogonal Dimensions, surpassing MobileNetV2
kssteven418/LTP
[KDD'22] Learned Token Pruning for Transformers
szq0214/S2-BNN
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)
SHI-Labs/Any-Precision-DNNs
Any-Precision Deep Neural Networks (AAAI 2021)
xvyaward/owq
Code for the AAAI 2024 Oral paper "OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models".
mit-han-lab/neurips-micronet
[JMLR'20] NeurIPS 2019 MicroNet Challenge Efficient Language Modeling, Champion
tiangexiang/BiX-NAS
[MICCAI 2021] BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
lironui/ABCNet
The semantic segmentation of remote sensing images
linksense/EfficientNet.PyTorch
Concise, Modular, Human-friendly PyTorch implementation of EfficientNet with Pre-trained Weights.
kssteven418/Q-ASR
[ICASSP'22] Integer-only Zero-shot Quantization for Efficient Speech Recognition
NoakLiu/GraphSnapShot
GraphSnapShot: Caching Local Structure for Fast Graph Learning [Efficient ML System]
Kthyeon/KAIST-AI-NeurIPS2019-MicroNet-2nd-place-solution
NeurIPSCD2019, MicroNet Challenge hosted by Google, Deepmind Researcher, "Efficient Model for Image Classification With Regularization Tricks".
gongzix/Lite-Mind
Official code base for ‘Lite-Mind : Towards Efficient and Robust Brain Representation Learning’
xternalz/SDPoint
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
linksense/MixNet-PyTorch
Concise, Modular, Human-friendly PyTorch implementation of MixNet with Pre-trained Weights.
tuanlda78202/cod
[Thesis'24] Efficient Class Incremental Learning for Object Detection
HolmesShuan/PyTorch-MixNet-SS
Extremely light-weight MixNet with Top-1 75.7% and 2.5M params
ozora-ogino/efficient_backbones
Implementation of efficient backbones for computer vision task.
bellthomas/VDQN
Exploring Variational Deep Q Networks. A study undertaken for the University of Cambridge's R244 Computer Science Masters Course. Inspired by https://arxiv.org/abs/1711.11225/.
Snigdho8869/DeepDream-StyleTransfer
Explore image transformations with DeepDream Algorithm and Neural Style Transfer in creative image processing.
01-vyom/melanoma-classification
Melanoma Classification using Semi-supervised learning
tarto-dev/tagsy-discord
Tagsy, your friendly Discord bot, designed to enhance server interaction with its intuitive tagging system
raja-hassan/calculator-js
JavaScript Calculator
sghawana/lenet-5-with-depthwise-convolution
Implemented LeNet-5 network proposed in paper titled "Gradient-based learning applied to document recognition" using PyTorch library in Python. Trained and tested the network using MNSIT dataset