Closed never-to-never closed 2 years ago
@never-to-never ONNX models profile to similar or better speeds in our benchmarks:
benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False
Checking setup...
YOLOv5 🚀 v6.1-39-gab2b1c0 torch 1.11.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 50.9/166.8 GB disk)
Benchmarks complete (488.23s)
Format mAP@0.5:0.95 Inference time (ms)
0 PyTorch 0.462296 8.556671
1 TorchScript 0.462296 5.429171
2 ONNX 0.462296 13.180766
3 OpenVINO 0.462296 73.727725
4 TensorRT 0.462280 1.643648
5 CoreML NaN NaN
6 TensorFlow SavedModel NaN NaN
7 TensorFlow GraphDef NaN NaN
8 TensorFlow Lite NaN NaN
9 TensorFlow Edge TPU NaN NaN
10 TensorFlow.js NaN NaN
benchmarks: weights=yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False
Checking setup...
YOLOv5 🚀 v6.1-39-gab2b1c0 torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 42.3/166.8 GB disk)
Benchmarks complete (472.19s)
Format mAP@0.5:0.95 Inference time (ms)
0 PyTorch 0.462296 109.502023
1 TorchScript 0.462296 141.845495
2 ONNX 0.462296 65.344190
3 OpenVINO 0.462296 68.984546
4 TensorRT NaN NaN
5 CoreML NaN NaN
6 TensorFlow SavedModel 0.462296 119.991329
7 TensorFlow GraphDef 0.462296 119.357180
8 TensorFlow Lite 0.462334 224.775610
9 TensorFlow Edge TPU NaN NaN
10 TensorFlow.js NaN NaN
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Question
Why is the ONNX model much slower than PT model in reasoning? Is there any way to improve the reasoning speed of the ONNX model?
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