Update Instructions

README.md

@@ -6,16 +6,18 @@
 
 <!--ACCURACY_PLACEHOLDER-->Accuracy: Stat: 98.01% (updated: 2025-04-28)<!--END_ACCURACY-->
 ## Short description
-A lightweight C++ library for performing high-performance inference on MNIST handwritten digits using a modified AlexNet architecture. Designed for efficiency and educational purposes, this project demonstrates how classic CNNs can be optimized for small-scale tasks in native environments.
+A lightweight C++ library for performing high-performance inference on classification tasks. Designed for efficiency and educational purposes, this project demonstrates how classic CNNs can be optimized for small-scale tasks in native environments.
 ### Key Features:
 
 * C++17 implementation for bare-metal performance
 
 * Simplified AlexNet for 28×28 grayscale images
 
+* Googlenet, Densenet, Resnet and Yolo11x-cls for images of any size
+
 * Parallel computing via Intel OneTBB (Threading Building Blocks)
 
-* Pre-trained model: AlexNet-model.h5 included
+* Pre-trained model: AlexNet-model.h5, Googlenet.onnx included
 ## **Some files used to create the library**
 ### Neural network models
 You need to download [Alexnet-model.h5](https://github.com/moizahmed97/Convolutional-Neural-Net-Designer/blob/master/AlexNet-model.h5) to the folder *docs*
@@ -30,9 +32,9 @@ Other models:</br>
 
 ## **How do I launch the inference?**
 * Make sure you install the project dependencies by running: *pip install -r requirements.txt*
-* You need to run the script *parser.py* that is located in app/AlexNet to read weights from a model *Alexnet-model.h5* and the json file with the weights will be stored in the *docs* folder.
+* You need to run the script *parser.py* that is located in app/converters to read weights from a model *Alexnet-model.h5* or *parser_onnx.py* to read weights from a models ONNX or YOLO and the json file with the weights will be stored in the *docs* folder.
 * Then put the test images in png format in the folder *docs/input*
-* After building the project, which is described below, run Graph_build in folder *build/bin*
+* After building the project, which is described below, run Graph_build with the parameter --model (alexnet_mnist or googlenet or densenet or resnet or yolo) and the parameter --parallel if you need. App Graph_build is located in folder *build/bin*
 
 ## **Building a Project**
 ### *Windows*
@@ -69,7 +71,7 @@ To build and run this project locally on Windows, follow these steps:
     ```
    and run the file
     ```bash
-   Graph_Build.exe
+   Graph_Build.exe --model alexnet_mnist
     ```
 ### *Linux/macOS*
    To build and run this project locally on Linux or macOS, follow these steps:
@@ -116,7 +118,7 @@ To build and run this project locally on Windows, follow these steps:
     ```
    and run the file
     ```bash
-   ./Graph_Build
+   ./Graph_Build --model alexnet_mnist
     ```
 
 ## Test Process
@@ -147,10 +149,14 @@ To start the testing process locally, you need to go to the directory
    ./run_test
    ```
 
-## **Accuracy validation**
+## **Accuracy validation for Alexnet on MNIST**
 To run accuracy validation you need to use the MNIST dataset, which you can download [here](https://github.com/DeepTrackAI/MNIST_dataset/tree/main/mnist/test) and put it in a folder *docs/mnist/mnist/test*
-Now you can run accuracy check - *build\bin\ACC_MNIST.exe*
-* **The accuracy should be 98.02%**
+Now you can run accuracy check - *build\bin\ACC.exe --model alexnet_mnist*
+* **The accuracy should be 98.01%**
+
+## **Accuracy validation for ONNX or YOLO models on ImageNet**
+To run accuracy validation you need to use the ImageNet dataset, which you can download [here](https://www.kaggle.com/datasets/sautkin/imagenet1kvalid) and put it in a folder *docs/Imagenet/*
+Now you can run accuracy check - *build\bin\ACC.exe --model googlenet*
 
 ## **Documentation of project**
 https://github.com/embedded-dev-research/ITLabAI/blob/Semyon1104/Final_documentation/docs/IT_Lab_2023.pdf