用YOLOv3 模型在一个开源的人手检测数据集oxford hand上做人手检测,并在此基础上做模型剪枝。对于该数据集,对 YOLOv3 进行 channel pruning 之后,模型的参数量、模型大小减少 80% ,FLOPs 降低 70%,前向推断的速度可以达到原来的 200%,同时可以保持 mAP 基本不变。 需要注意的是,项目代码的速度均是直接随机产生...
剪枝(Pruning):剪枝技术通过去除神经网络中冗余的连接,减小模型的复杂度,从而提高推理速度和减少存储需求。 3.3 YOLOv8在实际应用中的挑战 YOLOv8作为一个高效的目标检测模型,广泛应用于自动驾驶、智能安防、无人机视觉等领域,但在一些特定场景下,YOLOv8仍面临着挑战。 复杂场景下的检测准确性:在光照不良或遮挡严重的...
1.9万 49 13:28 App 神经网络剪枝 Neural Network Pruning 7400 37 08:36:44 App 这也太全了!绝对是B站最完整的【深度学习模型部署与剪枝优化】教程!PyTorch+TensorFlow+YOLO三大类型模型部署课程(matlab) 4922 51 03:42:02 App 深度学习模型部署与剪枝优化实战:迪哥手把手教你Pytotch、YOLO、Tensorflow三大深...
kernels tests ultralytics .gitignore .pre-commit-config.yaml CITATION.cff CONTRIBUTING.md LICENSE README.md README.zh-CN.md bus.jpg get_model_info.py mkdocs.yml pyproject.toml train.py val.py yolov8_pruning.py Latest commit Cannot retrieve latest commit at this time. ...
append(m) iterative_steps = 1 # progressive pruning pruner = tp.pruner.MagnitudePruner( model.model, example_inputs, importance=imp, iterative_steps=iterative_steps, ch_sparsity=0.5, # remove 50% channels ignored_layers=ignored_layers, unwrapped_parameters=unwrapped_parameters ) base_macs, base_...
python yolov8 分割模型输出不是xywh,之前的博客中已经实现了YOLOv4、YOLOR、YOLOX的剪枝,经过了几天的辛勤努力,终于实现了YOLOv5的剪枝。相关链接如下:YOLOv4剪枝(剪枝相关细节理论这里有写):YOLOv4剪枝YOLOX剪枝:YOLOX剪枝YOLOR剪枝:YOLOR剪枝Paper:PruningFilters
DeepSparse’s performance can be pushed even further by optimizing the model for inference. DeepSparse is built to take advantage of models that have been optimized with weight pruning and quantization—techniques that dramatically shrink the required compute without dropping accuracy. Through our One-...
基于DepGraph算法开发的PyTorch结构化剪枝框架 Torch-Pruning是一个结构化剪枝库,与现有框架(如torch.nn.utils.prune)最大区别在于,它会物理地移除参数,同时自动裁剪其他依赖层。 本课程在YOLOv8 v8.1版本代码的基础上增加DepGraph剪枝方法,在Windows和Ubuntu系统上演示针对自己的数据集训练和进行网络剪枝过程,并讲解Dep...
1. 前提 + 效果图 2. 更改步骤 2.1 得到PR_curve.csv和F1_curve.csv 2.1.1 YOLOv7的更改 2.1.1.1 得到PR_curve.csv 2.2.1.2 得到F1_curve.csv 2.1.2 YOLOv5的更改(v6.1版本) 2.1.3 YOLOv8的更改(附训练、验证方式) 2.2 绘制PR曲线 1. 前提 + 效果图 ...
Neural Magic's DeepSparse Engine is an inference runtime designed to optimize the execution of neural networks on CPUs through advanced techniques such as sparsity, pruning, and quantization. By integrating DeepSparse with YOLOv8, you can achieve GPU-like performance on standard CPUs, significantly ...