diff --git a/README.md b/README.md
index c02335e..45e38c9 100644
--- a/README.md
+++ b/README.md
@@ -7,45 +7,59 @@
-## Overview
-
-You can finally install [YOLOv5 object detector](https://github.com/ultralytics/yolov5) using [pip](https://pypi.org/project/yolov5/) and integrate into your project easily.
+## Overview
+
-## Installation
+## Install
-- Install yolov5 using pip `(for Python >=3.7)`:
+
+Install yolov5 using pip (for Python >=3.7)
```console
pip install yolov5
```
-- Install yolov5 using pip `(for Python 3.6)`:
+
+
+
+Install yolov5 using pip `(for Python 3.6)`
```console
pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
pip install yolov5
```
-## Basic Usage
+
+
+## Use from Python
+
+
+
+Basic
```python
import yolov5
-# model
+# load model
model = yolov5.load('yolov5s')
-# image
+# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
-# inference
+# perform inference
results = model(img)
# inference with larger input size
@@ -54,15 +68,24 @@ results = model(img, size=1280)
# inference with test time augmentation
results = model(img, augment=True)
-# show results
+# parse results
+predictions = results.pred[0]
+boxes = predictions[:, :4] # x1, x2, y1, y2
+scores = predictions[:, 4]
+categories = predictions[:, 5]
+
+# show detection bounding boxes on image
results.show()
-# save results
+# save results into "results/" folder
results.save(save_dir='results/')
```
-## Alternative Usage
+
+
+
+Alternative
```python
from yolov5 import YOLOv5
@@ -90,6 +113,12 @@ results = yolov5.predict(image1, augment=True)
# perform inference on multiple images
results = yolov5.predict([image1, image2], size=1280, augment=True)
+# parse results
+predictions = results.pred[0]
+boxes = predictions[:, :4] # x1, x2, y1, y2
+scores = predictions[:, 4]
+categories = predictions[:, 5]
+
# show detection bounding boxes on image
results.show()
@@ -97,11 +126,41 @@ results.show()
results.save(save_dir='results/')
```
-## Scripts
+
+
+
+Train/Detect/Test/Export
+
+- You can directly use these functions by importing them:
+
+```python
+from yolov5 import train, test, detect, export
+
+train.run(imgsz=640, data='coco128.yaml')
+test.run(imgsz=640, data='coco128.yaml', weights='yolov5s.pt')
+detect.run(imgsz=640)
+export.run(imgsz=640, weights='yolov5s.pt')
+```
+
+- You can pass any argument as input:
+
+```python
+from yolov5 import detect
+
+img_url = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
+
+detect.run(source=img_url, weights="yolov5s6.pt", conf_thres=0.25, imgsz=640)
+
+```
-You can call yolo_train, yolo_detect and yolo_test commands after installing the package via `pip`:
+
-### Training
+## Use from CLI
+
+You can call `yolo_train`, `yolo_detect`, `yolo_test` and `yolo_export` commands after installing the package via `pip`:
+
+
+Training
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).
@@ -112,7 +171,10 @@ $ yolo_train --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5x 16
```
-### Inference
+
+
+
+Inference
yolo_detect command 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`.
@@ -129,12 +191,4 @@ $ yolo_detect --source 0 # webcam
To run inference on example images in `yolov5/data/images`:
-```bash
-$ yolo_detect --source yolov5/data/images --weights yolov5s.pt --conf 0.25
-```
-
-## Status
-
-Builds for the latest commit for `Windows/Linux/MacOS` with `Python3.6/3.7/3.8`: 
-
-Status for the train/detect/test scripts: 
+
- 
+