ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)

Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

docs/yolov5/tutorials/model_ensembling.md

@@ -1,14 +1,14 @@
 ---
 comments: true
+description: Learn how to ensemble YOLOv5 models for improved mAP and Recall! Clone the repo, install requirements, and start testing and inference.
 ---
 
-📚  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):
 > Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. The motivation for using ensemble models is to reduce the generalization error of the prediction. As long as the base models are diverse and independent, the prediction error of the model decreases when the ensemble approach is used. The approach seeks the wisdom of crowds in making a prediction. Even though the ensemble model has multiple base models within the model, it acts and performs as a single model.
 
-
 ## Before You Start
 
 Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
@@ -22,11 +22,13 @@ pip install -r requirements.txt  # install
 ## Test Normally
 
 Before ensembling we want to establish the baseline performance of a single model. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. `yolov5x.pt` is the largest and most accurate model available. Other options are `yolov5s.pt`, `yolov5m.pt` and `yolov5l.pt`, or you own checkpoint from training a custom dataset `./weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
+
 ```bash
 python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half
 ```
 
 Output:
+
 ```shell
 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)
@@ -59,6 +61,7 @@ Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
 ## Ensemble Test
 
 Multiple pretrained models may be ensembled together at test and inference time by simply appending extra models to the `--weights` argument in any existing val.py or detect.py command. This example tests an ensemble of 2 models together:
+
 - YOLOv5x
 - YOLOv5l6
 
@@ -67,6 +70,7 @@ python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half
 ```
 
 Output:
+
 ```shell
 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)
@@ -101,11 +105,13 @@ Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
 ## Ensemble Inference
 
 Append extra models to the `--weights` argument to run ensemble inference:
+
 ```bash
 python detect.py --weights yolov5x.pt yolov5l6.pt --img 640 --source data/images
 ```
 
 Output:
+
 ```bash
 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)
@@ -121,8 +127,8 @@ image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 3 persons, 2 ties, Don
 Results saved to runs/detect/exp2
 Done. (0.223s)
 ```
-<img src="https://user-images.githubusercontent.com/26833433/124489091-ea4f9a00-ddb0-11eb-8ef1-d6f335c97f6f.jpg" width="500">
 
+<img src="https://user-images.githubusercontent.com/26833433/124489091-ea4f9a00-ddb0-11eb-8ef1-d6f335c97f6f.jpg" width="500">
 
 ## Environments
 
@@ -133,7 +139,6 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with
 - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
 - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
 
-
 ## Status
 
 <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>