YOLOv7 推理加速實(shí)戰(zhàn)OpenVINO 與 TorchORT 兩種流程對(duì)比這篇教程根據(jù)我復(fù)現(xiàn) YOLOv7 推理加速流程時(shí)整理重點(diǎn)演示環(huán)境安裝、數(shù)據(jù)準(zhǔn)備、普通模型推理、TorchORT 推理和結(jié)果可視化。本文整理自我的學(xué)習(xí)和項(xiàng)目復(fù)現(xiàn)過程盡量按實(shí)操順序保留 notebook 的關(guān)鍵步驟同時(shí)把數(shù)據(jù)集獲取方式調(diào)整為適合中文教程發(fā)布的寫法。本文會(huì)重點(diǎn)跑通以下流程安裝 YOLOv7 環(huán)境從數(shù)據(jù)集后臺(tái)獲取測試數(shù)據(jù)掛載訓(xùn)練權(quán)重目錄運(yùn)行普通推理流程安裝 TorchORT 并運(yùn)行加速推理如果你正在系統(tǒng)學(xué)習(xí)目標(biāo)檢測、實(shí)例分割、OCR、多目標(biāo)跟蹤或視覺大模型建議收藏本文配套 notebook、示例圖片和運(yùn)行環(huán)境說明后續(xù)會(huì)繼續(xù)整理。如果環(huán)境配置卡住可以在評(píng)論區(qū)說明具體報(bào)錯(cuò)。 文章目錄YOLOv7 推理加速實(shí)戰(zhàn)OpenVINO 與 TorchORT 兩種流程對(duì)比?? 安裝 YOLOv7 安裝 PyTorch 準(zhǔn)備測試數(shù)據(jù) 掛載權(quán)重目錄 普通權(quán)重推理? 普通推理結(jié)果 安裝 TorchORT? TorchORT 推理? TorchORT 結(jié)果 小結(jié) 同系列教程匯總?? 安裝 YOLOv7克隆 YOLOv7 倉庫并安裝依賴。# 下載 YOLOv7 倉庫并安裝依賴!git clone https://github.com/WongKinYiu/yolov7.git%cd yolov7 !pip install-r requirements.txt 安裝 PyTorch安裝 notebook 指定的 PyTorch 版本。!pip install torch1.12.1 準(zhǔn)備測試數(shù)據(jù)從數(shù)據(jù)集后臺(tái)獲取 YOLOv7 格式數(shù)據(jù)并配置本地路徑。fromtypesimportSimpleNamespace# 從數(shù)據(jù)集后臺(tái)下載 YOLOv7 檢測 格式數(shù)據(jù)集后修改 DATASET_DIR 指向解壓目錄。DATASET_DIR/content/dataset# 修改為數(shù)據(jù)集后臺(tái)導(dǎo)出的數(shù)據(jù)集目錄datasetSimpleNamespace(locationDATASET_DIR,version1,namecustom-dataset) 掛載權(quán)重目錄掛載云盤或設(shè)置本地權(quán)重路徑。# 掛載云盤或設(shè)置本地權(quán)重路徑。fromgoogle.colabimportdrive drive.mount(/content/gdrive)TRAINED_MODEL_PATH/content/gdrive/MyDrive/TrainedModel/best.pt 普通權(quán)重推理使用普通 YOLOv7 推理腳本運(yùn)行樣例圖片。# If you DO NOT have a retrained model, use this command to run evaluation with the provided trained model:!python detect_without_jit.py--weights/content/yolov7/runs/best.pt--conf0.25--img-size640--source UWH-6/test/images/DJI_0021_mp4-32_jpg.rf.0d9b746d8896d042b55a14c8303b4f36.jpg# Run evaluation on a sample image with a retrained model!python detect_without_jit.py--weights/content/gdrive/MyDrive/TrainedModel/best.pt--conf0.25--img-size640--source UWH-6/test/images/DJI_0021_mp4-32_jpg.rf.0d9b746d8896d042b55a14c8303b4f36.jpg? 普通推理結(jié)果展示普通推理結(jié)果圖片。# 展示樣例圖片推理結(jié)果importglobfromIPython.displayimportImage,display i0limit10000# max images to print# Change the path below according to the location where result image is saved.forimageNameinglob.glob(/content/yolov7/runs/detect/exp/*.jpg):#assuming JPG.ifilimit:display(Image(filenameimageName))print(\n)ii1 安裝 TorchORT安裝 TorchORT 推理依賴。# Install torch-ort-infer!pip install torch-ort-infer? TorchORT 推理使用 TorchORT 推理腳本運(yùn)行樣例圖片。# If you DO NOT have a retrained model, use this command to run evaluation using the provided trained model:!python detect_ort.py--weights/content/yolov7/runs/best.pt--conf0.25--img-size640--source UWH-6/test/images/DJI_0021_mp4-32_jpg.rf.0d9b746d8896d042b55a14c8303b4f36.jpg# Run evaluation on a sample image with a retrained model!python detect_ort.py--weights/content/gdrive/MyDrive/TrainedModel/best.pt--conf0.25--img-size640--source UWH-6/test/images/DJI_0021_mp4-32_jpg.rf.0d9b746d8896d042b55a14c8303b4f36.jpg? TorchORT 結(jié)果展示 TorchORT 推理結(jié)果圖片。# 展示樣例圖片推理結(jié)果importglobfromIPython.displayimportImage,display i0limit10000# max images to printforimageNameinglob.glob(/content/yolov7/runs/detect/exp2/*.jpg):#assuming JPGifilimit:display(Image(filenameimageName))print(\n)ii1 小結(jié)這篇教程完整整理了YOLOv7 OpenVINO 與 TorchORT 推理的核心復(fù)現(xiàn)流程。實(shí)際操作時(shí)建議先確認(rèn) GPU、依賴版本、數(shù)據(jù)集路徑和模型權(quán)重路徑再逐段運(yùn)行 notebook。后續(xù)我會(huì)繼續(xù)按源項(xiàng)目順序整理同系列中的目標(biāo)檢測、實(shí)例分割、OCR、多目標(biāo)跟蹤和視覺大模型教程。 同系列教程匯總Google Gemini 3.5 Flash 零樣本目標(biāo)檢測教程從提示詞到可視化結(jié)果GLM-OCR 文檔識(shí)別實(shí)戰(zhàn)教程從驗(yàn)證碼、公式到車牌 OCRRF-DETR ByteTrack 多目標(biāo)跟蹤實(shí)戰(zhàn)教程從命令行到 Python 視頻軌跡可視化SAM 3 圖像分割實(shí)戰(zhàn)教程文本、框和點(diǎn)提示的多種分割方式Y(jié)OLOv7 推理加速實(shí)戰(zhàn)OpenVINO 與 TorchORT 兩種流程對(duì)比