NN_Test

This project contains some deep learning code(Note: depend on opencv/libtorch/onnxruntime):

• deep learning code(C++)
  • Perceptron
  • BP(Back Propagation)
  • CNN(Convolutional Neural Networks)
  • Linear Regression(gradient descent、least squares)
  • Naive Bayes Classifier(sex classification)
  • Logistic Regression(gradient descent, Batch/Mini Batch)
  • KNN(K-Nearest Neighbor, classification)
  • PCA(Principal Components Analysis)
  • single hidden layer(two categories)
  • K-Means
  • Decision Tree(CART)
  • YOLOv8/YOLO11(OpenCV DNN, libtorch, onnxruntime, OpenVINO)
    • Classify
    • Object Detection
    • Instance Segmentation
    • OBB
• mathematical formula's implement with C++
  • Linear Algebra
    • transpose
    • determinant
    • adjoint matrix
    • inverse matrix
    • norm(vector、matrix)
    • eigenvalues/eigenvectors(real symmetric matrix)
    • SVD(Singular Value Decomposition)
    • pseudoinverse
    • trace
  • Probability
    • mean、variance、standard deviation
    • covariance matrix
  • Activation Function
    • logistic sigmoid function
    • softplus function
    • ReLU(rectified linear unit)
    • Leaky ReLUs(leaky rectified linear units)
    • ELUs(Exponential linear units)
    • softmax function
    • tanh(Hyperbolic tangent)
  • Loss Function
    • MSE(Mean Square Error)
    • Cross-Entropy
  • Optimization
    • BGD(Batch Gradient Descent)
    • SGD(Stochastic Gradient Descent)
    • MBGD(Mini-batch Gradient Descent)
    • SGD with Momentum
    • AdaGrad(Adaptive Gradient)
    • RMSProp(Root Mean Square Propagation)
    • Adadelta(an adaptive learning rate method)
    • Adam(Adaptive Moment Estimation)
    • AdaMax(a variant of Adam based on the infinity norm)
    • NAG(Nesterov Accelerated Gradient)
    • Nadam(Nesterov-accelerated Adaptive Moment Estimation)
  • Layers
    • Dropout
    • LRN(Local Response Normalization)
    • BN(Batch Normalization)
  • common algorithm
    • Brute Force(string match)
    • Triangle similarity(monocular ranging)
• Database convert to Images or Images conver to Database
  • MNIST
  • CIFAR10/CIFAR100
  • ORL Faces Database
• open source library's usage(C++)
  • tiny-cnn GitHub
  • tiny-dnn GitHub
  • libsvm GitHub
  • tensorrt infer GitHub
  • ollama-hppGitHub
  • OpenCV GitHub
    • Logistic Regression
    • Support Vector Machines(SVM)
    • KNN(K-Nearest Neighbor)
    • Decision Tree
    • PCA(Principal Components Analysis)
    • k-means
• Python code(support windows and linux platform)
  • Universal Configuration
    • Installation(Anaconda)

       conda create -n NN_Test python=3.9
       conda activate NN_Test
       git clone https://github.com/fengbingchun/NN_Test
       cd NN_Test/demo/Python
       pip install -r requirements.txt
      

    • Code
      • KNN(K-Nearest Neighbor)
      • Decision Tree(CART)
      • PCA(Principal Components Analysis)
      • k-means
        • k-means
        • k-menas opencv
      • txt to csv
      • XGBoost Regress
      • K-Fold Cross-Validation
      • McNemar's Test
      • Ensemble Learning
  • MediaPipe
    • Installation(Anaconda)

       conda create -n MediaPipe python=3.9
       conda activate MediaPipe
       pip install mediapipe
       pip install requests
       git clone https://github.com/fengbingchun/NN_Test
       cd NN_Test/demo/Python
      

    • Code
      • face monocular ranging
  • Ollama
    • Installation(Anaconda)

       conda create --name ollama python=3.10 -y
       conda activate ollama
       pip install ollama
       pip install colorama chromadb tqdm
       pip install sentence-transformers
       pip install langchain==1.0.0 langchain-huggingface langchain-chroma langchain-ollama jq
       pip install torch==2.6.0 transformers==4.57.0 torchvision==0.21.0 opencv-python==4.10.0.84 timm sentencepiece scikit-image
       pip install accelerate>=0.26.0
       pip install labelme2coco onnx
      

    • Code
      • sync ollama interface
      • ollama rag
      • ollama rag: sentence_transformers
      • langchain rag
      • Transformers pipeline
      • Transformers Detect model convert to ONNX
      • Transformers Detect model train
      • CocoDataset class
      • PyTorch set seed
  • Ultralytics
    • Installation(Anaconda):Note:after configuring according to the following commands, you need to downgrade the versions of OpenCV and NumPy: opencv-python: 4.10.0.84; numpy: 1.26.4

       # cpu
       conda create --name ultralytics-env python=3.10 -y
       conda activate ultralytics-env
       pip install ultralytics==8.4.56
       pip install colorama
       pip install openvino==2026.0.0 # keep the same version as libopenvino
       pip install nncf==2.8.0 # openvino INT8 need
      
       # cuda
       # install cuda 11.8
       # install cudnn v8.7.0: copy the contents of bin,include,lib/x64 cudnn directories to the corresponding CUDA directories
       # install tensorrt 8.5.3.1:
       #	copy the contents of bin,include,lib/*.lib,lib/*.dll tensorrt directories to the corresponding CUDA directories
       # 	enter the python directory: pip install tensorrt-8.5.3.1-cp310-none-win_amd64.whl
       conda create --name ultralytics-env-cuda python=3.10 -y
       conda activate ultralytics-env-cuda
       conda install pytorch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 pytorch-cuda=11.8 -c pytorch -c nvidia
       pip install ultralytics==8.4.56
       pip install colorama
       pip install openvino==2026.0.0 # keep the same version as libopenvino
       pip install nncf==2.8.0 # openvino INT8 need
       pip install openpyxl # write excel file
       pip install moviepy # parse video file
       pip install torch-pruning==1.6.0
       pip install scikit-image
      
       git clone https://github.com/fengbingchun/NN_Test
       cd NN_Test/demo/Python
      

    • Code
      • LabelMe JSON to YOLOv8 TXT: Detect
      • EISeg JSON to YOLOV8 TXT: Segment
      • YOLOv8/YOLO11/YOLO26 train: detect/segment/classify/obb/semantic/pose/depth
      • YOLOv8/YOLO11/YOLO26 predict: detect/segment/classify/obb/semantic/pose/depth
      • YOLOv8 segment onnxruntime predict
      • image classify: include AlexNet, ResNet, DenseNet
      • YOLOv8 preprocess: detect/segment
      • YOLOv8 postprocess: detect/segment
      • PyTorch learning rate warmup
      • model pruning: Torch-Pruning
      • knowledge distillation: classify
      • model quantization: PTQ
      • image similarity algorithms
      • PyTorch model convert to OpenVINO
      • TorchMetrics's usage
  • PaddleOCR
    • Installation(Anaconda)

       conda create --name paddleocr python=3.10 -y
       conda activate paddleocr
       pip install paddlepaddle==3.3.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ # only CPU
       pip install paddlepaddle-gpu==3.3.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # CUDA 11.8
       pip install paddleocr==3.5.0 # only basic OCR and document image preprocessing capabilities are needed
       pip install "paddleocr[all]" # requires all optional capabilities such as document parsing, document comprehension, document translation, and key information extraction
      

    • Code
      • character recognition

The project support platform:

• windows10 64 bits: It can be directly build with VS2022 in windows10 64bits.
• Linux:
  • DatasetToImage support cmake build(file position: prj/linux_cmake_DatasetToImage)
  • Math support cmake build(file position: prj/linux_cmake_Math)
  • tiny-cnn support cmake build(file position: prj/linux_cmake_tiny-cnn)
  • tiny-dnn support cmake build(file position: prj/linux_cmake_tiny-dnn)
  • NN support cmake build(file position: prj/linux_cmake_NN)

Windows VS Screenshot:

Blog: fengbingchun