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rtdetr-spscd_buoy_joint_1280_b8_e100 – AI Model by ARG-NCTU | AlphaNeural AI

You can deploy this model and start earning money today!

ARG-NCTU/
rtdetr-spscd_buoy_joint_1280_b8_e100

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object-detection

ARG-NCTU/Split_Port_Ship_Classification_Dataset_coco

ARG-NCTU/Singapore_Maritime_Dataset_coco

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RT-DETR Training Results

Model Information

Model: rtdetr-l.pt
Dataset: spscd_plus_buoy_yolo/dataset.yaml
Classes: 13
Image Size: 1280×1280
Batch Size: 8
Epochs: 100
Workers: 16

Training Configuration

Initial Learning Rate: 0.0001
Final Learning Rate Factor: 0.01
Patience: 50
Mixed Precision (AMP): Enabled

Training Time

Start: 2025-12-16 02:36:48
End: 2025-12-17 12:51:39
Duration: 34h 14m 50s (123,290 seconds)

Final Test Results

mAP50: 0.9919
mAP50-95: 0.8531

Files in this Repository

best.pt - Best model checkpoint
results.csv - Complete training metrics
results.png - Training curves visualization
confusion_matrix.png - Confusion matrix
F1_curve.png, PR_curve.png - Performance curves
samples/ - Sample validation predictions

Note: TensorBoard logs are included if available. You can also visualize training using results.csv and the curve images.

How to Use

python

from ultralytics import RTDETR

# Load model
model = RTDETR('best.pt')

# Run inference
results = model('your_image.jpg')

Visualizing Training Progress

View training curves in results.png
Analyze per-class performance in confusion matrices
Check results.csv for detailed epoch-by-epoch metrics