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rtdetr-spscd_smd13clsbuoy_finetuned_e50 – AI Model by ARG-NCTU | AlphaNeural AI

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ARG-NCTU/
rtdetr-spscd_smd13clsbuoy_finetuned_e50

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object-detection

ARG-NCTU/Split_Port_Ship_Classification_Dataset_coco

ARG-NCTU/SMD_13cls_yolo

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RT-DETR Training Results

Model Information

Model: runs/rtdetr/spscd_base_1280_b8_e100/weights/best.pt
Dataset: finetune_dataset.yaml
Classes: 13
Image Size: 1280×1280
Batch Size: 8
Epochs: 50
Workers: 16

Training Configuration

Initial Learning Rate: 1e-05
Final Learning Rate Factor: 0.01
Patience: 50
Mixed Precision (AMP): Enabled

Training Time

Start: 2026-01-12 00:01:08
End: 2026-01-12 08:37:43
Duration: 8h 36m 35s (30,995 seconds)

Final Test Results

mAP50: 0.1615
mAP50-95: 0.1314

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
gpu_stats.csv - GPU monitoring data (utilization, memory, temperature, power) recorded per epoch

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