📝 [Update] README, with more run code example

README.md

@@ -1,14 +1,14 @@
 # YOLO: Official Implementation of YOLOv9, YOLOv7
 
 ![GitHub License](https://img.shields.io/github/license/WongKinYiu/YOLO)
-![WIP](https://img.shields.io/badge/status-WIP-orange)
 [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/henry000/YOLO)
-> [!IMPORTANT]
+<!-- ![WIP](https://img.shields.io/badge/status-WIP-orange) -->
+<!-- > [!IMPORTANT]
 > This project is currently a Work In Progress and may undergo significant changes. It is not recommended for use in production environments until further notice. Please check back regularly for updates.
 >
 > Use of this code is at your own risk and discretion. It is advisable to consult with the project owner before deploying or integrating into any critical systems.
 
-Welcome to the official implementation of YOLOv7 and YOLOv9. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.
+Welcome to the official implementation of YOLOv7 and YOLOv9. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9. -->
 
 ## TL;DR
 - This is the official YOLO model implementation with an MIT License.
@@ -65,7 +65,8 @@ To train YOLO on your dataset:
 1. Modify the configuration file `data/config.yaml` to point to your dataset.
 2. Run the training script:
 ```shell
-python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c
+python yolo/lazy.py dataset=dev use_wandb=True
+python yolo/lazy.py task.data.batch_size=8 model=v9-c # or more args
 ```
 
 ### Transfer Learning
@@ -77,15 +78,24 @@ python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={datase
 ### Inference
 To evaluate the model performance, use:
 ```shell
-python yolo/lazy.py task=inference weight=weights/v9-c.pt model=v9-c task.fast_inference=deploy # use deploy weight
 python yolo/lazy.py task=inference # if cloned from GitHub
+python yolo/lazy.py task=inference \
+                    name=AnyNameYouWant \ # AnyNameYouWant
+                    device=cpu \ # hardware cuda, cpu, mps
+                    model=v9-s \ # model version: v9-c, m, s
+                    task.nms.min_confidence=0.1 \ # nms config
+                    task.fast_inference=onnx \ # onnx, trt, deploy
+                    task.data.source=data/toy/images/train \ # path to file, dir, webcam
+                    +quite=True \ # Quite Output
 yolo task=inference task.data.source={Any} # if pip installed
 ```
 
-### Validation [WIP]
+### Validation
 To validate the model performance, use:
 ```shell
-# Work In Progress...
+python yolo/lazy.py task=validation
+# or
+python yolo/lazy.py task=validation dataset=toy
 ```
 
 ## Contributing

docs/HOWTO.md

@@ -2,6 +2,30 @@
 
 To facilitate easy customization of the YOLO model, we've structured the codebase to allow for changes through configuration files and minimal code adjustments. This guide will walk you through the steps to customize various components of the model including the architecture, blocks, data loaders, and loss functions.
 
+## Examples
+
+```shell
+# Train
+python yolo/lazy.py dataset=dev use_wandb=True
+
+# Validate
+python yolo/lazy.py task=validation
+python yolo/lazy.py task=validation model=v9-s
+python yolo/lazy.py task=validation dataset=toy
+python yolo/lazy.py task=validation dataset=toy name=validation
+
+# Inference
+python yolo/lazy.py task=inference
+python yolo/lazy.py task=inference device=cpu
+python yolo/lazy.py task=inference +quite=True
+python yolo/lazy.py task=inference name=AnyNameYouWant
+python yolo/lazy.py task=inference image_size=\[480,640]
+python yolo/lazy.py task=inference task.nms.min_confidence=0.1
+python yolo/lazy.py task=inference task.fast_inference=deploy
+python yolo/lazy.py task=inference task.fast_inference=onnx device=cpu
+python yolo/lazy.py task=inference task.data.source=data/toy/images/train
+```
+
 ## Custom Model Architecture
 
 You can change the model architecture simply by modifying the YAML configuration file. Here's how: