Project1 Yolo Baseline Yolo11n

--- license: mit library_name: ultralytics tags: - yolo - ultralytics - spectrum-recognition - object-detection - course-project pipeline_tag: object-detection --- # Project 1 YOLO Baseline - yolo11n This repository contains the trained YOLO baseline checkpoint for the wireless spectrum recognition course project. ## Model - Base model: `yolo11n.pt` - Task: object detection on spectrogram images - Classes: single class, `signal` - Input size: 640 - Batch size: 16 - Training device: single RTX 3090 GPU - Training schedule: 50 epochs with early stopping, stopped at epoch 32 ## Dataset Setup The original H5 labels provide frequency ranges but no explicit time ranges or protocol classes. The baseline therefore uses a conservative conversion: - Every frequency band is treated as one detection target. - Bounding boxes cover the full time axis. - The vertical position and height are computed from the frequency range. - All boxes are assigned to the single class `signal`. Final split: - Train: 19200 images - Val: 4800 images - Test: 6000 images The test split comes from an independent `test_full.h5`, not from the training set. ## Validation Best Epoch Best validation fitness and best `mAP50-95` occurred at epoch 12: - Precision: 0.87967 - Recall: 0.63125 - mAP50: 0.69653 - mAP50-95: 0.59204 Best validation `mAP50` occurred at epoch 14: - mAP50: 0.70082 ## Real Test Metrics Evaluation with `best.pt` on the independent test set: - Precision: 0.59541 - Recall: 0.32303 - mAP50: 0.35692 - mAP50-95: 0.23215 The gap between validation and independent test performance suggests limited generalization under the current single-class full-width-box approximation. ## Files - `best.pt`: trained YOLO checkpoint - `results.csv`: full training log exported by Ultralytics - `metrics.json`: independent test metrics - `latency.json`: latency benchmark - `results.png`: training curves - `confusion_matrix.png`: validation confusion matrix ## Known Limitations - This is a single-class detector, not a protocol classifier. - The current H5 parser does not recover signal categories because the available labels only expose frequency bands. - The full-width box assumption is a baseline approximation and should be replaced if time boundaries become available.

Source context: 25 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo javadcc/project1-yolo-baseline-yolo11n