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

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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>
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6
docs/yolov5/tutorials/model_pruning_and_sparsity.md
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@ -1,5 +1,6 @@
---
comments: true
description: Learn how to apply pruning to your YOLOv5 models. See the before and after performance with an explanation of sparsity and more.
---
📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models.
@ -18,11 +19,13 @@ pip install -r requirements.txt # install
## Test Normally
Before pruning 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=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, 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, dnn=False
YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
@ -58,6 +61,7 @@ We repeat the above test with a pruned model by using the `torch_utils.prune()`
<img width="894" alt="Screenshot 2022-02-02 at 22 54 18" src="https://user-images.githubusercontent.com/26833433/152243799-b0ac2777-b1a8-47b1-801a-2e4c93c06ead.png">
30% pruned output:
```bash
val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, 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, dnn=False
YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
@ -89,7 +93,6 @@ Results saved to runs/val/exp3
In the results we can observe that we have achieved a **sparsity of 30%** in our model after pruning, which means that 30% of the model's weight parameters in `nn.Conv2d` layers are equal to 0. **Inference time is essentially unchanged**, while the model's **AP and AR scores a slightly reduced**.
## 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):
@ -99,7 +102,6 @@ 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>