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Treffer: ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference

Title:
ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference
Authors:
Glenn Jocher, Ayush Chaurasia, Alex Stoken, Jirka Borovec, NanoCode012, Yonghye Kwon, TaoXie, Jiacong Fang, imyhxy, Kalen Michael, Lorna, Abhiram V, Diego Montes, Jebastin Nadar, Laughing, tkianai, yxNONG, Piotr Skalski, Zhiqiang Wang, Adam Hogan, Cristi Fati, Lorenzo Mammana, AlexWang1900, Deep Patel, Ding Yiwei, Felix You, Jan Hajek, Laurentiu Diaconu, Mai Thanh Minh
Publisher Information:
Zenodo
Publication Year:
2022
Collection:
Zenodo
Document Type:
E-Ressource software
Language:
unknown
Relation:
https://github.com/ultralytics/yolov5/tree/v6.1; https://zenodo.org/records/6222936; oai:zenodo.org:6222936; https://doi.org/10.5281/zenodo.6222936
DOI:
10.5281/zenodo.6222936
Availability:
https://doi.org/10.5281/zenodo.6222936
https://zenodo.org/records/6222936
Rights:
Other (Open) ; other-open
Accession Number:
edsbas.43FAC2B6
Database:
BASE

Weitere Informationen

This release incorporates new features and bug fixes (271 PRs from 48 contributors) since our last release in October 2021. It adds TensorRT, Edge TPU and OpenVINO support, and provides retrained models at --batch-size 128 with new default one-cycle linear LR scheduler. YOLOv5 now officially supports 11 different formats, not just for export but for inference (both detect.py and PyTorch Hub), and validation to profile mAP and speed results after export. Format export.py --include Model PyTorch - yolov5s.pt TorchScript torchscript yolov5s.torchscript ONNX onnx yolov5s.onnx OpenVINO openvino yolov5s_openvino_model/ TensorRT engine yolov5s.engine CoreML coreml yolov5s.mlmodel TensorFlow SavedModel saved_model yolov5s_saved_model/ TensorFlow GraphDef pb yolov5s.pb TensorFlow Lite tflite yolov5s.tflite TensorFlow Edge TPU edgetpu yolov5s_edgetpu.tflite TensorFlow.js tfjs yolov5s_web_model/ Usage examples (ONNX shown): Export: python export.py --weights yolov5s.pt --include onnx Detect: python detect.py --weights yolov5s.onnx PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx') Validate: python val.py --weights yolov5s.onnx Visualize: https://netron.app Important Updates TensorRT support: TensorFlow, Keras, TFLite, TF.js model export now fully integrated using python export.py --include saved_model pb tflite tfjs (https://github.com/ultralytics/yolov5/pull/5699 by @imyhxy) Tensorflow Edge TPU support ⭐ NEW: New smaller YOLOv5n (1.9M params) model below YOLOv5s (7.5M params), exports to 2.1 MB INT8 size, ideal for ultralight mobile solutions. (https://github.com/ultralytics/yolov5/pull/3630 by @zldrobit) OpenVINO support: YOLOv5 ONNX models are now compatible with both OpenCV DNN and ONNX Runtime (https://github.com/ultralytics/yolov5/pull/6057 by @glenn-jocher). Export Benchmarks: Benchmark (mAP and speed) all YOLOv5 export formats with python utils/benchmarks.py --weights yolov5s.pt. Currently operates on CPU, future updates will implement GPU support. ...