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fcl_rtdetr_r10_e100_480
ARG-NCTU/fcl_rtdetr_r10_e100_480
fcl_rtdetr_r10_e100_480 is a machine learning model from ARG-NCTU. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
- Model: best.pt - Dataset: CL0: SMD / CL1: SPSCD - Classes: 6(SPSCD) + 6(Shared) + 4(SMD) - Image Size: 480 - Total Training Epochs: 100 - Total Rounds: 10
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Public
Updated Jan 26, 2026
Repo size
882 MB
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At a glance
- Access
- Public
- Created
- Jan 26, 2026
- Updated
- Jan 26, 2026
- SHA
- 29eb835f
Model card
From the Hugging Face model README
FCL RT-DETR
Model Information
- Model:
best.pt - Dataset: CL0: SMD / CL1: SPSCD
- #Classes: 6(SPSCD) + 6(Shared) + 4(SMD)
- Image Size: 480
- Total Training Epochs: 100
- Total Rounds: 10
Training Time
- CL0: SMD
- Hardware: NVIDIA RTX 5070 12GB
- Total Duration: 401h 4m 46s (1,443,887s)
- Average per Epoch: 240.6 minutes
- CL1: SPSCD
- Hardware: NVIDIA RTX 5090 32GB
- Total Duration: 205h 30m 6s (739,806s)
- Average per Epoch: 123.3 minutes
Global Validation Results
- last_rnd_global/:
- BoxF1_curve.png
- BoxPR_curve.png
- confusion_matrix_normalized.png
- confusion_matrix.png
Repository Structure
c0_smd/: Result for SMD training stage.c1_spscd/: Result for SPSCD training stage.last_rnd_global/: Global validation metrics & curves (F1, PR, Confusion Matrix).weights/: Containsbest.ptandlast.pt.
How to Use
from ultralytics import RTDETR
# Load model
model = RTDETR('best.pt')
# Run inference
results = model('your_image.jpg')
- Created
- Jan 26, 2026
- Updated
- Jan 26, 2026