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Update export format docstrings (#6151)
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* Update export documentation

* Cleanup

* Update export.py

* Update README.md

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58 changes: 31 additions & 27 deletions README.md
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Expand Up @@ -62,24 +62,24 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on tr
<details open>
<summary>Install</summary>

[**Python>=3.6.0**](https://www.python.org/) is required with all
[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
[**Python>=3.6.0**](https://www.python.org/) environment, including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).

```bash
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```

</details>

<details open>
<summary>Inference</summary>

Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).

```python
import torch
Expand All @@ -104,34 +104,38 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
<details>
<summary>Inference with detect.py</summary>

`detect.py` runs inference on a variety of sources, downloading models automatically from
the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.

```bash
$ python detect.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
python detect.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```

</details>

<details>
<summary>Training</summary>

Run commands below to reproduce results
on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the
largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
results. [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). Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
largest `--batch-size` possible, or pass `--batch-size -1` for
YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.

```bash
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40
yolov5l 24
yolov5x 16
python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```

<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
Expand Down Expand Up @@ -225,6 +229,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
### Pretrained Checkpoints

[assets]: https://github.com/ultralytics/yolov5/releases

[TTA]: https://github.com/ultralytics/yolov5/issues/303

|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
Expand Down Expand Up @@ -257,7 +262,6 @@ We love your input! We want to make contributing to YOLOv5 as easy and transpare

<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>


## <div align="center">Contact</div>

For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
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24 changes: 18 additions & 6 deletions detect.py
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Expand Up @@ -2,14 +2,26 @@
"""
Run inference on images, videos, directories, streams, etc.
Usage:
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
Usage - sources:
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python path/to/detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel # CoreML (under development)
yolov5s_openvino_model # OpenVINO (under development)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow protobuf
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s.engine # TensorRT
"""

import argparse
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26 changes: 14 additions & 12 deletions export.py
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Expand Up @@ -2,18 +2,19 @@
"""
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
Format | Example | `--include ...` argument
--- | --- | ---
PyTorch | yolov5s.pt | -
TorchScript | yolov5s.torchscript | `torchscript`
ONNX | yolov5s.onnx | `onnx`
CoreML | yolov5s.mlmodel | `coreml`
OpenVINO | yolov5s_openvino_model/ | `openvino`
TensorFlow SavedModel | yolov5s_saved_model/ | `saved_model`
TensorFlow GraphDef | yolov5s.pb | `pb`
TensorFlow Lite | yolov5s.tflite | `tflite`
TensorFlow.js | yolov5s_web_model/ | `tfjs`
TensorRT | yolov5s.engine | `engine`
Format | Example | `--include ...` argument
--- | --- | ---
PyTorch | yolov5s.pt | -
TorchScript | yolov5s.torchscript | `torchscript`
ONNX | yolov5s.onnx | `onnx`
CoreML | yolov5s.mlmodel | `coreml`
OpenVINO | yolov5s_openvino_model/ | `openvino`
TensorFlow SavedModel | yolov5s_saved_model/ | `saved_model`
TensorFlow GraphDef | yolov5s.pb | `pb`
TensorFlow Lite | yolov5s.tflite | `tflite`
TensorFlow Edge TPU | yolov5s_edgetpu.tflite | `edgetpu`
TensorFlow.js | yolov5s_web_model/ | `tfjs`
TensorRT | yolov5s.engine | `engine`
Usage:
$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml openvino saved_model tflite tfjs
Expand All @@ -27,6 +28,7 @@
yolov5s_saved_model
yolov5s.pb
yolov5s.tflite
yolov5s_edgetpu.tflite
yolov5s.engine
TensorFlow.js:
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11 changes: 9 additions & 2 deletions train.py
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@@ -1,10 +1,17 @@
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Train a YOLOv5 model on a custom dataset
Train a YOLOv5 model on a custom dataset.
Models and datasets download automatically from the latest YOLOv5 release.
Models: https://github.com/ultralytics/yolov5/tree/master/models
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
Usage:
$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (RECOMMENDED)
$ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
"""

import argparse
import math
import os
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14 changes: 13 additions & 1 deletion val.py
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Expand Up @@ -3,7 +3,19 @@
Validate a trained YOLOv5 model accuracy on a custom dataset
Usage:
$ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640
$ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
Usage - formats:
$ python path/to/val.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel # CoreML (under development)
yolov5s_openvino_model # OpenVINO (under development)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow protobuf
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s.engine # TensorRT
"""

import argparse
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