A framework to train a ResUNet architecture, quantize, compile and execute it on an FPGA.
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Updated
Jun 23, 2023 - Jupyter Notebook
A framework to train a ResUNet architecture, quantize, compile and execute it on an FPGA.
Research experiments archive for post-training quantization with TensorRT. Submitted and Accepted to IEEE EDGE 2024
Post-Training quantization perfomed on the model trained with CLIC dataset.
Post post-training-quantization (PTQ) method for improving LLMs. Unofficial implementation of https://arxiv.org/abs/2309.02784
Quantization for Object Detection in Tensorflow 2.x
EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.
Model Quantization with Pytorch, Tensorflow & Larq
Comprehensive study on the quantization of various CNN models, employing techniques such as Post-Training Quantization and Quantization Aware Training (QAT).
The repository discusses a research work published on MDPI Sensors and provides details about the project.
Low-bit (2/4/8/16) Post Training Quantization for ResNet20
Generating tensorrt model using onnx
Post-training quantization on Nvidia Nemo ASR model
quantization example for pqt & qat
Implementation of EPTQ - an Enhanced Post-Training Quantization algorithm for DNN compression
This sample shows how to convert TensorFlow model to OpenVINO IR model and how to quantize OpenVINO model.
Improved the performance of 8-bit PTQ4DM expecially on FID.
Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization
[ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models"
[CVPR 2024 Highlight] TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
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