75 lines
1.7 KiB
Markdown
75 lines
1.7 KiB
Markdown
# RoAdCast - Flask + PyTorch CNN starter
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This project contains a minimal Flask app and a small PyTorch CNN scaffold so you can train and run a model directly from VS Code.
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Quick setup (Windows PowerShell):
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1. Create and activate a virtual environment
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```powershell
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python -m venv .venv
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.\.venv\Scripts\Activate.ps1
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```
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2. Install dependencies
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```powershell
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pip install -r requirements.txt
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```
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3. Dataset layout
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Image dataset (folder-per-class):
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data/
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class1/
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img1.jpg
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class2/
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img2.jpg
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CSV dataset (single file):
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data.csv (expects a `label` column and numeric feature columns)
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Train commands:
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Image training (default cnn):
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```powershell
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python train.py data --epochs 5 --batch-size 16
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```
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CSV/tabular training (MLP):
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```powershell
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python train.py data.csv --model-type mlp --epochs 20 --batch-size 64
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```
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The model will be saved as `model.pth` in the repo root (best validation checkpoint).
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Run the Flask app (for local testing):
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```powershell
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python app.py
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```
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Predict using curl (or Postman). Example with curl in PowerShell:
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```powershell
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curl -X POST -F "image=@path\to\image.jpg" http://127.0.0.1:5000/predict
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```
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VS Code tips
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- Open this folder in VS Code.
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- Use the Python extension and select the `.venv` interpreter.
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- Use the Run panel to add a launch configuration that runs `app.py` or `train.py`.
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- For long training runs, run training in the terminal (not the debugger) and monitor logs.
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Notes & next steps
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- The SimpleCNN uses 224x224 input and expects at least two maxpool steps; adjust `models.py` if you want smaller inputs.
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- Add better transforms and augmentation in `data.py` for better performance.
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- If GPU is available, PyTorch will use it automatically if installed with CUDA.
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