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/test_time_augmentation.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn how to use Test Time Augmentation (TTA) with YOLOv5 to improve mAP and Recall during testing and inference. Code examples included.
 ---
 
 # Test-Time Augmentation (TTA)
@@ -20,11 +21,13 @@ pip install -r requirements.txt  # install
 ## Test Normally
 
 Before trying TTA we want to establish a baseline performance to compare to. 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)
@@ -55,12 +58,15 @@ Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
 ```
 
 ## Test with TTA
+
 Append `--augment` to any existing `val.py` command to enable TTA, and increase the image size by about 30% for improved results. Note that inference with TTA enabled will typically take about 2-3X the time of normal inference as the images are being left-right flipped and processed at 3 different resolutions, with the outputs merged before NMS. Part of the speed decrease is simply due to larger image sizes (832 vs 640), while part is due to the actual TTA operations.
+
 ```bash
 python val.py --weights yolov5x.pt --data coco.yaml --img 832 --augment --half
 ```
 
 Output:
+
 ```shell
 val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=832, conf_thres=0.001, iou_thres=0.6, task=val, device=, single_cls=False, augment=True, 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)
@@ -94,11 +100,13 @@ Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json...
 ## Inference with TTA
 
 `detect.py` TTA inference operates identically to `val.py` TTA: simply append `--augment` to any existing `detect.py` command:
+
 ```bash
 python detect.py --weights yolov5s.pt --img 832 --source data/images --augment
 ```
 
 Output:
+
 ```bash
 detect: weights=['yolov5s.pt'], source=data/images, imgsz=832, 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=True, 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)
@@ -116,10 +124,10 @@ Done. (0.156s)
 
 <img src="https://user-images.githubusercontent.com/26833433/124491703-dbb6b200-ddb3-11eb-8b57-ed0d58d0d8b4.jpg" width="500">
 
-
 ### PyTorch Hub TTA
 
 TTA is automatically integrated into all [YOLOv5 PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5) models, and can be accessed by passing `augment=True` at inference time.
+
 ```python
 import torch
 
@@ -136,11 +144,10 @@ results = model(img, augment=True)  # <--- TTA inference
 results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
 ```
 
-### Customize 
+### Customize
 
 You can customize the TTA ops applied in the YOLOv5 `forward_augment()` method [here](https://github.com/ultralytics/yolov5/blob/8c6f9e15bfc0000d18b976a95b9d7c17d407ec91/models/yolo.py#L125-L137).
 
-
 ## Environments
 
 YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
@@ -150,7 +157,6 @@ 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>