jahongir7174/YOLOv8-qat: Quantization Aware Training - GitHub github.com › jahongir7174 › YOLOv8-qat
Quantization Aware Training. Implementation of YOLOv8 without DFL using PyTorch. Installation: conda create -n YOLO, python=3.8, conda activate YOLO. |
29 нояб. 2023 г. · If you require quantization-aware training (QAT) specifically, you might need to implement a custom training loop using TensorFlow or PyTorch ... |
27 сент. 2023 г. · There are mainly two Quantization methods that are firstly, PTQ (Post Training Quantization), which does not require additional training, and ... |
Neural Magic optimizes YOLO11 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models ... ONNX · TFLite · TensorRT · Intel OpenVINO Export |
This tutorial demonstrates step-by-step instructions on how to run apply quantization with accuracy control to PyTorch YOLOv8. |
Videolar Quantization-aware Training is a popular method that allows quantizing a model and applying fine-tuning to restore accuracy degradation caused by quantization. |
24 янв. 2024 г. · The article explores the concept of quantization in machine learning, detailing how it reduces the bit representation of data in models. |
6 февр. 2024 г. · Quantization Aware Training involves training/ finetuning a model with quantized parameters. QAT can help to improve the model performance while quantization. |
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