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

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11
docs/yolov5/tutorials/model_export.md
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@ -1,5 +1,6 @@
---
comments: true
description: Export YOLOv5 models to TFLite, ONNX, CoreML, and TensorRT formats. Achieve up to 5x GPU speedup using TensorRT. Benchmarks included.
---
# TFLite, ONNX, CoreML, TensorRT Export
@ -41,10 +42,10 @@ YOLOv5 inference is officially supported in 11 formats:
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov5s_web_model/` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov5s_paddle_model/` |
## Benchmarks
Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>. To reproduce:
```bash
python benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0
```
@ -98,6 +99,7 @@ Benchmarks complete (241.20s)
## Export a Trained YOLOv5 Model
This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. `yolov5s.pt` is the 'small' model, the second-smallest model available. Other options are `yolov5n.pt`, `yolov5m.pt`, `yolov5l.pt` and `yolov5x.pt`, along with their P6 counterparts i.e. `yolov5s6.pt` or you own custom training checkpoint i.e. `runs/exp/weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
```bash
python export.py --weights yolov5s.pt --include torchscript onnx
```
@ -105,6 +107,7 @@ python export.py --weights yolov5s.pt --include torchscript onnx
💡 ProTip: Add `--half` to export models at FP16 half precision for smaller file sizes
Output:
```bash
export: data=data/coco128.yaml, weights=['yolov5s.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 🚀 v6.2-104-ge3e5122 Python-3.7.13 torch-1.12.1+cu113 CPU
@ -137,10 +140,10 @@ The 3 exported models will be saved alongside the original PyTorch model:
[Netron Viewer](https://github.com/lutzroeder/netron) is recommended for visualizing exported models:
<p align="center"><img width="850" src="https://user-images.githubusercontent.com/26833433/191003260-f94011a7-5b2e-4fe3-93c1-e1a935e0a728.png"></p>
## Exported Model Usage Examples
`detect.py` runs inference on exported models:
```bash
python detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
@ -156,6 +159,7 @@ python detect.py --weights yolov5s.pt # PyTorch
```
`val.py` runs validation on exported models:
```bash
python val.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
@ -171,6 +175,7 @@ python val.py --weights yolov5s.pt # PyTorch
```
Use PyTorch Hub with exported YOLOv5 models:
``` python
import torch
@ -200,6 +205,7 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
## OpenCV DNN inference
OpenCV inference with ONNX models:
```bash
python export.py --weights yolov5s.pt --include onnx
@ -232,7 +238,6 @@ 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>