极智项目 | YOLO11目标检测算法训练+TensorRT部署实战

datasets/└── coco_mintrain_10k/ ├── annotations/ │ ├── instances_train2017.json │ ├── instances_val2017.json │ ├── ... (其他标注文件) ├── train2017/ │ ├── 000000000001.jpg │ ├── ... (其他训练图像) ├── val2017/ │ ├── 000000000001.jpg │ ├── ... (其他验证图像) └── test2017/ ├── 000000000001.jpg ├── ... (其他测试图像)

conda creaet -n yolo11_py310 python=3.10
conda activate yolo11_py310
pip install -U -r train/requirements.txt

bash 0_download_wgts.sh

bash 1_run_predict_yolo11.sh

bash 2_run_train_yolo11.sh

# Load a modelmodel = YOLO(curr_path + "/wgts/yolo11n.pt")
# Train the modeltrain_results = model.train( data= curr_path + "/cfg/coco128.yaml", # path to dataset YAML epochs=100, # number of training epochs imgsz=640, # training image size device="0", # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu)
# Evaluate model performance on the validation setmetrics = model.val()

bash 3_run_export_onnx.sh

tar zxvf TensorRT-xxx-.tar.gz
# 软链trtexecsudo ln -s /path/to/TensorRT/bin/trtexec /usr/local/bin# 验证一下trtexec --help
# 安装trt的python接口cd pythonpip install tensorrt-xxx.whl

bash 4_build_trt_engine.sh

[10/02/2024-21:28:48] [V] === Explanations of the performance metrics ===[10/02/2024-21:28:48] [V] Total Host Walltime: the host walltime from when the first query (after warmups) is enqueued to when the last query is completed.[10/02/2024-21:28:48] [V] GPU Compute Time: the GPU latency to execute the kernels for a query.[10/02/2024-21:28:48] [V] Total GPU Compute Time: the summation of the GPU Compute Time of all the queries. If this is significantly shorter than Total Host Walltime, the GPU may be under-utilized because of host-side overheads or data transfers.[10/02/2024-21:28:48] [V] Throughput: the observed throughput computed by dividing the number of queries by the Total Host Walltime. If this is significantly lower than the reciprocal of GPU Compute Time, the GPU may be under-utilized because of host-side overheads or data transfers.[10/02/2024-21:28:48] [V] Enqueue Time: the host latency to enqueue a query. If this is longer than GPU Compute Time, the GPU may be under-utilized.[10/02/2024-21:28:48] [V] H2D Latency: the latency for host-to-device data transfers for input tensors of a single query.[10/02/2024-21:28:48] [V] D2H Latency: the latency for device-to-host data transfers for output tensors of a single query.[10/02/2024-21:28:48] [V] Latency: the summation of H2D Latency, GPU Compute Time, and D2H Latency. This is the latency to infer a single query.[10/02/2024-21:28:48] [I] &&&& PASSED TensorRT.trtexec [TensorRT v100500] [b18] # trtexec --onnx=../wgts/yolo11n_sim.onnx --saveEngine=../wgts/yolo11n.engine --fp16 --verbose

bash 5_infer_trt.sh

 ------ trt infer success! ------