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Parameters
31.9M
FLOPs
87.5G
Input Size
640px
License
Apache-2.0
Architecture
Type
one-stage
Backbone
NAS-Optimized
Neck
QSP
Head
Decoupled
Benchmark Results
Performance on COCO val2017 across different hardware configurations
| Hardware | mAP@50-95 | FPS | Latency | VRAM |
|---|---|---|---|---|
| NVIDIA A100 (TensorRT FP16) | 51.4% | 104.1 | 9.6ms | 1462 MB |
| NVIDIA T4 (TensorRT FP16) | 51.4% | 40.4 | 24.7ms | 1388 MB |
| CPU (ONNX Runtime) | 51.4% | 4.6 | 216.6ms | 1293 MB |
Speed Breakdown (A100 TensorRT)
End-to-end latency breakdown showing preprocessing, inference, and postprocessing times
1.5ms
5.8ms
2.3ms
Preprocess
Inference
Postprocess (NMS)
Usage with LibreYOLO
from libreyolo import YOLO
# Load model
model = YOLO.from_pretrained("https://huggingface.co/Libre-YOLO/yolo-nas-m")
# Run inference
results = model.predict("image.jpg")
# Process results
for box in results.boxes:
print(f"Class: {box.cls}, Confidence: {box.conf:.2f}")nas-optimizedbalanced
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