https://github.com/kadirnar/yolov6-pip
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Papers with Code 아카이브
Quantization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
- 32
Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).
Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kbountro/cSAX
https://github.com/keerthi165/depthEstimation
Deep attention-based classification network for robust depth prediction
https://github.com/ken-power/SensorFusionND-3D-Object-Tracking
https://github.com/kevin-rn/Efficient-Fact-checking
Unsupervised Neural Quantization for Compressed-Domain Similarity Search
https://github.com/khawar512/ImageCompression
Image Compression with Recurrent Neural Network and Generalized Divisive Normalization
https://github.com/khoadoan106/single_loss_quantization
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching
https://github.com/knowlab/bi-weekly-paper-presentation
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
https://github.com/kohjingyu/fromage
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
https://github.com/koujm/convert-tf
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/kpu/fastertext
https://github.com/krishnap25/mauve
https://github.com/kssteven418/Q-ASR
Integer-only Zero-shot Quantization for Efficient Speech Recognition
https://github.com/kuanzi/Bayesian_quant
https://github.com/kuleshov-group/llmtune
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
https://github.com/leeyegy/simcc
SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation
https://github.com/lifeiteng/naturalspeech3_facodec
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
https://github.com/linkfluence/fastText4j
https://github.com/linyang-zhh/FQ-ViT
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
https://github.com/liutianlin0121/seislm
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/lmbxmu/SiMaN
https://github.com/lmcache/lmcache
CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving
https://github.com/lmd1993/fastTextBoost
https://github.com/loveheaven/scann
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
https://github.com/lovepan1/caffe_ssd_traffic
https://github.com/lucamocerino/Binary-Neural-Networks-PyTorch-1.0
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
https://github.com/lucidrains/magvit2-pytorch
https://github.com/luckyPT/jvm-ml
https://github.com/luhuiguo/jfasttext
https://github.com/lukesin/nas-for-kws-2
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization
https://github.com/lxa9867/R2VOS
https://github.com/lxa9867/r2bench
https://github.com/lz20061213/darknet-ameliorate
Quantizing deep convolutional networks for efficient inference: A whitepaper
https://github.com/lzhengchun/TomoGAN
https://github.com/maheshkaran/Nervana-Distiller
Fast Adjustable Threshold For Uniform Neural Network Quantization (Winning solution of LPIRC-II)
https://github.com/mailong25/self-supervised-speech-recognition
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.