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Parameters
3.2M
FLOPs
8.7G
Input Size
640px
License
MIT
Architecture
Type
one-stage
Backbone
CSPDarknet
Neck
PAFPN
Head
Decoupled
Benchmark Results
Performance on COCO val2017 across different hardware configurations
| Hardware | mAP@50-95 | FPS | Latency | VRAM |
|---|---|---|---|---|
| NVIDIA A100 (TensorRT FP16) | 37.4% | 193.3 | 5.2ms | 179 MB |
| NVIDIA T4 (TensorRT FP16) | 37.4% | 80.3 | 12.5ms | 202 MB |
| CPU (ONNX Runtime) | 37.4% | 10.4 | 96.6ms | 193 MB |
Speed Breakdown (A100 TensorRT)
End-to-end latency breakdown showing preprocessing, inference, and postprocessing times
1.4ms
1.4ms
2.3ms
Preprocess
Inference
Postprocess (NMS)
Usage with LibreYOLO
from libreyolo import YOLO
# Load model
model = YOLO.from_pretrained("https://huggingface.co/Libre-YOLO/yolov8n")
# Run inference
results = model.predict("image.jpg")
# Process results
for box in results.boxes:
print(f"Class: {box.cls}, Confidence: {box.conf:.2f}")production-readyreal-timeedge-friendly
Notes
Nano variant, best for edge deployment