HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.0.141 create new SettingsManager (#3790)

Browse source
This commit is contained in:
Glenn Jocher 2023-07-23 16:03:34 +02:00 • committed by GitHub
parent 42afe772d5
commit 20f5efd40a
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
215 changed files with 917 additions and 749 deletions
Download patch file Download diff file Expand all files Collapse all files

14
docs/yolov5/tutorials/model_ensembling.md
View file

@ -4,7 +4,7 @@ description: Learn how to ensemble YOLOv5 models for improved mAP and Recall! Cl
keywords: YOLOv5, object detection, ensemble learning, mAP, Recall
---
📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and Recall.
📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and Recall.
UPDATED 25 September 2022.
From [https://en.wikipedia.org/wiki/Ensemble_learning](https://en.wikipedia.org/wiki/Ensemble_learning):
@ -34,7 +34,7 @@ Output:
val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
Fusing layers...
Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients
val: Scanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2846.03it/s]
@ -76,9 +76,9 @@ Output:
val: data=./data/coco.yaml, weights=['yolov5x.pt', 'yolov5l6.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
Fusing layers...
Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients # Model 1
Fusing layers...
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients # Model 2
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt'] # Ensemble notice
@ -117,9 +117,9 @@ Output:
detect: weights=['yolov5x.pt', 'yolov5l6.pt'], source=data/images, imgsz=640, conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_width=3, hide_labels=False, hide_conf=False, half=False
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
Fusing layers...
Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients
Fusing layers...
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']
@ -144,4 +144,4 @@ YOLOv5 is designed to be run in the following up-to-date verified environments (
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.