Improve classification comments
diff --git a/.github/README_cn.md b/.github/README_cn.md
index 86b502df61f..816adf6b044 100644
--- a/.github/README_cn.md
+++ b/.github/README_cn.md
@@ -269,7 +269,7 @@ We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4x
 <details>
   <summary>Table Notes (click to expand)</summary>
 
-- All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 at image size 224 and all default settings. Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2.
+- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
 - **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
 - **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
 - **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
@@ -291,14 +291,14 @@ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/trai
 ```
 
 ### Val
-Validate accuracy on a pretrained model. To validate YOLOv5s-cls accuracy on ImageNet.
+Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
 ```bash
 bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
-python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
 ```
 
 ### Predict
-Run a classification prediction on an image.
+Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
 ```bash
 python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
 ```
@@ -307,7 +307,7 @@ model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load
 ```
 
 ### Export
-Export a group of trained YOLOv5-cls, ResNet and EfficientNet models to ONNX and TensorRT.
+Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
 ```bash
 python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
 ```
diff --git a/README.md b/README.md
index b368d1d6e26..7335394402d 100644
--- a/README.md
+++ b/README.md
@@ -278,7 +278,7 @@ We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4x
 <details>
   <summary>Table Notes (click to expand)</summary>
 
-- All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 at image size 224 and all default settings. Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2.
+- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
 - **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
 - **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
 - **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
@@ -300,14 +300,14 @@ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/trai
 ```
 
 ### Val
-Validate accuracy on a pretrained model. To validate YOLOv5s-cls accuracy on ImageNet.
+Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
 ```bash
 bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
-python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
 ```
 
 ### Predict
-Run a classification prediction on an image.
+Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
 ```bash
 python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
 ```
@@ -316,7 +316,7 @@ model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load
 ```
 
 ### Export
-Export a group of trained YOLOv5-cls, ResNet and EfficientNet models to ONNX and TensorRT.
+Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
 ```bash
 python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
 ```
diff --git a/classify/predict.py b/classify/predict.py
index 419830d4395..4247e3c8e7f 100644
--- a/classify/predict.py
+++ b/classify/predict.py
@@ -3,7 +3,7 @@
 Run classification inference on images
 
 Usage:
-    $ python classify/predict.py --weights yolov5s-cls.pt --source im.jpg
+    $ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
 """
 
 import argparse
diff --git a/classify/train.py b/classify/train.py
index f2b46556744..b85f1423603 100644
--- a/classify/train.py
+++ b/classify/train.py
@@ -2,8 +2,10 @@
 """
 Train a YOLOv5 classifier model on a classification dataset
 Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/custom/dataset'
+YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
+Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
 
-Usage:
+Usage - Single-GPU and Multi-GPU DDP
     $ python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 128
     $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
 """
diff --git a/classify/val.py b/classify/val.py
index 0930ba8c9c5..9d965d9f1fd 100644
--- a/classify/val.py
+++ b/classify/val.py
@@ -3,7 +3,8 @@
 Validate a classification model on a dataset
 
 Usage:
-    $ python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet
+    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
 """
 
 import argparse



## 🛠️ PR Summary

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

### 🌟 Summary
Enhancement of documentation and examples for the Ultralytics YOLOv5 classification models.

### 📊 Key Changes
- Updated training checkpoint details, now including `weight_decay` parameter.
- Transitioned from showcasing the validation of the YOLOv5s-cls model to the YOLOv5m-cls model on the ImageNet-1k dataset.
- Predict section now suggests using the pretrained YOLOv5s-cls model to predict a single image (`bus.jpg`).
- Clarified the `Export` section by specifying the export of YOLOv5s-cls, ResNet, and EfficientNet models.
- Various documentation updates across the `classify` sub-module for clarity and consistency in instructions.

### 🎯 Purpose & Impact
- The added `weight_decay` parameter in documentation helps users understand model training configurations better.
- Highlighting the YOLOv5m-cls model provides insights into using a different category of pre-trained weights, possibly indicating a more robust or accurate model.
- Clarified examples make it easier for users to follow how to run individual tasks such as validating, predicting, and exporting models.
- The changes can lead to better user adoption and efficient usage of the YOLOv5 classification models, spreading knowledge on how to engage with the classification aspect of the Ultralytics ecosystem. 🚀