VB Project Eye — Step-by-Step Tutorial for Beginners

VB Project Eye: Complete Guide to Building an Image Recognition App

Overview

VB Project Eye is a Visual Basic-based image recognition application that detects and classifies objects in images. This guide walks through planning, required tools, core concepts, step-by-step implementation, sample code snippets, testing, and deployment—so you can build a working image recognition app using Visual Basic (VB.NET) and modern machine learning libraries.

What you’ll build

• A desktop VB.NET app that loads images, runs a pre-trained image classification model, displays top predictions with confidence scores, and saves results.
• Optional features: webcam capture, batch processing, and simple training/transfer learning workflow.

Tools & libraries

• IDE: Visual Studio 2022 or later (Community edition is fine).
• Language: VB.NET (target .NET ⁄₇ or later).
• ML runtime: ONNX Runtime (recommended) or ML.NET for easier .NET integration.
• Pre-trained model: MobileNetV2, ResNet50, or custom ONNX model trained for your classes. Use models from ONNX Model Zoo or export from PyTorch/TensorFlow to ONNX.
• Imaging: System.Drawing.Common or OpenCV (via Emgu CV) for image preprocessing and webcam support.
• Optional: NuGet packages: Microsoft.ML.OnnxRuntime, System.Drawing.Common, Emgu.CV (if using OpenCV), Newtonsoft.Json (for config/results).

Architecture & data flow

1. UI (WinForms/WPF) — image selection, camera capture, and result display.
2. Preprocessor — resize, normalize, and convert image to model input tensor.
3. Inference engine — run ONNX model with ONNX Runtime and get raw outputs.
4. Postprocessor — apply softmax, map indices to labels, pick top-N results.
5. Storage — log results to JSON or CSV; optionally save annotated images.

Step-by-step implementation

1) Project setup

• Create a new VB.NET WinForms or WPF project in Visual Studio (.NET 6 or 7).
• Add NuGet packages:
  • Microsoft.ML.OnnxRuntime
  • System.Drawing.Common
  • Newtonsoft.Json (optional)
  • Emgu.CV (optional for webcam)

2) UI layout

• Main window components:
  • PictureBox (or Image control) to show selected image.
  • Button: “Load Image” — opens file dialog.
  • Button: “Use Webcam” — starts webcam capture (optional).
  • Button: “Run Recognition” — invokes inference.
  • ListBox or Label area to show top predictions and confidence.
  • ProgressBar or status label for processing state.

3) Load model and labels

• Place your ONNX model file (e.g., mobilenetv2.onnx) in the project or a known path.
• Load class labels from a text file (one label per line) into a string array.

Sample VB.NET snippet to load labels:

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Dim labels() As String = IO.File.ReadAllLines(“labels.txt”) 

Load ONNX model session:

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Imports Microsoft.ML.OnnxRuntime 
Dim session As InferenceSession = New InferenceSession(“mobilenetv2.onnx”) 

4) Image preprocessing

• Typical steps: load image → resize to model input (e.g., 224×224) → convert to float32 → normalize (mean/std) → reorder channels if needed → create tensor.

Example function to create input tensor (assume RGB, 224×224, float32):

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Imports System.Drawing Imports Microsoft.ML.OnnxRuntime.TensorsFunction ImageToTensor(img As Bitmap, targetW As Integer, targetH As Integer) As DenseTensor(Of Single)

 
Dim resized As New Bitmap(img, New Size(targetW, targetH)) Dim tensor As New DenseTensor(Of Single)(New Integer() {1, 3, targetH, targetW}) For y As Integer = 0 To targetH - 1     For x As Integer = 0 To targetW - 1         Dim px As Color = resized.GetPixel(x, y)         ' Normalize to 0-1 and optionally apply mean/std         tensor(0, 0, y, x) = px.R / 255.0F         tensor(0, 1, y, x) = px.G / 255.0F         tensor(0, 2, y, x) = px.B / 255.0F     Next Next Return tensor 
End Function 

Adjust channel order/normalization according to your model.
5) Run inference

Prepare input container and run:

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Imports Microsoft.ML.OnnxRuntime Imports Microsoft.ML.OnnxRuntime.Tensors

 
Dim inputName As String = session.InputMetadata.Keys.First() Dim tensor = ImageToTensor(myBitmap, 224, 224) Dim inputs = New List(Of NamedOnnxValue) From {
 
NamedOnnxValue.CreateFromTensor(Of Single)(inputName, tensor) 
} Using results = session.Run(inputs)
Dim outputName = session.OutputMetadata.Keys.First() Dim outputTensor = results.First().AsEnumerable(Of Single)().ToArray() ' process outputTensor 
End Using 

6) Postprocessing

Convert raw logits to probabilities using softmax, then map top indices to labels:

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Function Softmax(logits() As Single) As Single()     Dim max = logits.Max()

 
Dim exps = logits.Select(Function(l) Math.Exp(l - max)).ToArray() Dim sumExp = exps.Sum() Return exps.Select(Function(e) CType(e / sumExp, Single)).ToArray() 
End Function 


Pick top-5:

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Dim probs = Softmax(outputTensor) Dim topN = probs.Select(Function(p, i) New With {Key .Index = i, Key .Prob = p}) _

 
           .OrderByDescending(Function(x) x.Prob).Take(5) 


7) Display results

Show labels and confidence in UI list, e.g., “Tabby cat — 93.5%”.
Optionally draw bounding boxes (if using object detection model) or overlay text on image and save.

8) Webcam support (optional)

Use Emgu.CV or OpenCV to capture frames, then run inference on each frame at a reduced frame rate for performance.
Be careful to run inference on a background thread to keep UI responsive.

9) Performance tips

Use a lightweight model (MobileNet, EfficientNet-lite) for real-time apps.
Use GPU-accelerated ONNX Runtime if available (install CUDA/DirectML execution providers).
Resize and batch inputs efficiently; reuse memory buffers where possible.
Run inference on a background thread or Task to avoid freezing UI.

10) Optional: Transfer learning / custom training

If you need custom classes, fine-tune a model in PyTorch/TensorFlow, export to ONNX, then use in VB app.
For small datasets, use transfer learning on MobileNet/ResNet head; augment images and validate carefully.

Example project structure

/models/mobilenetv2.onnx
/labels/labels.txt
/src/MainForm.vb (UI & event handlers)
/src/Inference.vb (model load, preprocess, postprocess)
/output/results.json (inference logs)

Testing & validation

Validate with a held-out test set; compute accuracy, precision/recall where applicable.
Test different lighting conditions and image sizes.
Check model robustness to rotations, blurring, and occlusion.

Deployment

Publish as a self-contained .NET desktop app (single-folder).
Include model and label files or allow remote model updates via secure download.
If distributing widely, provide clear instructions for installing GPU runtime if using GPU acceleration.

Troubleshooting

Wrong predictions: check preprocessing (channel order, normalization).
Poor performance: use smaller model or enable GPU execution provider.
Model load errors: confirm ONNX ops compatibility with chosen runtime.

Next steps and enhancements

Add object detection (e.g., YOLO/SSD ONNX models) for bounding boxes.
Build a simple annotation tool to collect labeled images for retraining.
Add cloud inference option (secure API) for heavier models if local resources are limited.

This guide gives a practical, end-to-end path to build VB Project Eye—an image recognition desktop app in VB.NET using ONNX Runtime. Use the code snippets and architecture notes as a foundation, adapt preprocessing to your model, and iterate on model choice for your accuracy/performance needs.