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Quick Start

Get started with Spectrum Analyzer in minutes.

Installation​

Prerequisites​

  • Python 3.8 or higher
  • CUDA-capable GPU (recommended)
  • PyTorch 2.0+ with CUDA support

Step 1: Clone Repository​

git clone https://github.com/type1compute/Spectrum-Analyzer.git
cd Spectrum-Analyzer

Repository: GitHub

Step 2: Install Dependencies​

pip install -r requirements.txt

Step 3: Install PyTorch​

Install PyTorch with CUDA support:

# For CUDA 11.8+
pip install torch>=2.0.0+cu118 torchvision>=0.15.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118

# Or for CUDA 12.1+
pip install torch>=2.0.0+cu121 torchvision>=0.15.0+cu121 --extra-index-url https://download.pytorch.org/whl/cu121

Using Pre-trained Model​

Download Pre-trained Model​

Pre-trained models are available at: Google Drive

Run Detection​

python detect.py \
    --weights path/to/best.pt \
    --source path/to/images \
    --imgsz 512 \
    --conf-thres 0.25 \
    --save-img true

Results will be saved to runs/detect/exp/

Training on Your Dataset​

Step 1: Prepare Dataset​

Organize your dataset:

dataset/
├── images/
│   ├── train/
│   ├── val/
│   └── test/
└── labels/
    ├── train/
    ├── val/
    └── test/

Step 2: Create Dataset Config​

Create data/your_dataset.yaml:

path: /path/to/dataset
train: images/train
val: images/val
test: images/test
nc: 11  # number of classes
names: ['class1', 'class2', ...]

Step 3: Train Model​

python train.py \
    --data data/your_dataset.yaml \
    --cfg models/resnet18.yaml \
    --imgsz 512 \
    --epochs 300 \
    --batch-size 64 \
    --device 0

For detailed training instructions, see the Training Guide.

Step 4: Validate Model​

python val.py \
    --weights runs/train/exp/weights/best.pt \
    --data data/your_dataset.yaml \
    --task test \
    --imgsz 512

For more validation options, see Configuration. For detailed training instructions, see the Training Guide.

Next Steps​