Update export format docstrings
diff --git a/README.md b/README.md
index fa0645d4fd2..59abd084572 100644
--- a/README.md
+++ b/README.md
@@ -62,15 +62,14 @@ 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>
@@ -78,8 +77,9 @@ $ pip install -r requirements.txt
 <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
@@ -104,17 +104,17 @@ 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>
@@ -122,16 +122,20 @@ $ python detect.py --source 0  # webcam
 <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">
@@ -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)
@@ -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
diff --git a/detect.py b/detect.py
index e6e74ea7dfe..1393f79746f 100644
--- a/detect.py
+++ b/detect.py
@@ -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
diff --git a/export.py b/export.py
index a0758010e81..67e32305ded 100644
--- a/export.py
+++ b/export.py
@@ -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
@@ -27,6 +28,7 @@
                                          yolov5s_saved_model
                                          yolov5s.pb
                                          yolov5s.tflite
+                                         yolov5s_edgetpu.tflite
                                          yolov5s.engine
 
 TensorFlow.js:
diff --git a/train.py b/train.py
index 304c001b654..bd2fb5898cb 100644
--- a/train.py
+++ b/train.py
@@ -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
diff --git a/val.py b/val.py
index c1fcf61b468..f7c9ef5e60d 100644
--- a/val.py
+++ b/val.py
@@ -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

General docstring improvements.

## 🛠️ PR Summary

<sub>Made with ❤️ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub>

### 🌟 Summary
Simplification and expansion of installation, inference, and training instructions; add model format options for detect.py and val.py.

### 📊 Key Changes
- Streamlined install instructions with a more concise format.
- Inference instructions now clarify that models automatically download from the latest release.
- detect.py supports a wider range of model formats, such as TorchScript, ONNX, TensorFlow SavedModel, etc.
- Training instructions mention YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092) feature and provide an updated table for batch sizes on V100-16GB GPUs.
- Added model format usage examples for val.py, mirroring the additions to detect.py.
  
### 🎯 Purpose & Impact
- Simplified instructions aim to improve the user experience in setting up and using YOLOv5.
- Expanded model format support in detect.py and val.py allows for more versatility and ease of deployment across different platforms and devices.
- These changes make YOLOv5 more accessible to a broader audience, facilitating the adoption and integration of the model into various projects and applications.