New YOLOv5 Classification Models

.github/README_cn.md

@@ -171,26 +171,23 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12
 ## <div align="center">如何与第三方集成</div>
 
 <div align="center">
-    <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
-    </a>
-    <a href="https://roboflow.com/?ref=ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
-    </a>
+  <a href="https://bit.ly/yolov5-deci-platform">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width="10%" /></a>
 </div>
 
-|Weights and Biases|Roboflow ⭐ 新|
-|:-:|:-:|
-|通过 [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) 自动跟踪和可视化你在云端的所有YOLOv5训练运行状态。|标记并将您的自定义数据集直接导出到YOLOv5，以便用 [Roboflow](https://roboflow.com/?ref=ultralytics) 进行训练。 |
-
-<!-- ## <div align="center">Compete and Win</div>
+|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
+|:-:|:-:|:-:|:-:|
+|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)
 
-We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
-
-<p align="center">
-  <a href="https://github.com/ultralytics/yolov5/discussions/3213">
-  <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
-</p> -->
 
 ## <div align="center">为什么选择 YOLOv5</div>
 
@@ -239,6 +236,84 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 
 </details>
 
+
+## <div align="center">Classification ⭐ NEW</div>
+
+YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started.
+
+<details>
+  <summary>Classification Checkpoints (click to expand)</summary>
+
+<br>
+
+We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
+
+| Model                                                                                              | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
+|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
+| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |
+| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |
+| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |
+| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |
+| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |
+|                                                                                                    |
+| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |
+| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |
+| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |
+| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |
+|                                                                                                    |
+| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |
+| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |
+| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |
+| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |
+
+<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.
+- **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`
+</details>
+</details>
+
+<details>
+  <summary>Classification Usage Examples (click to expand)</summary>
+
+### Train
+YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
+
+```bash
+# Single-GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# Multi-GPU DDP
+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
+```
+
+### Val
+Validate accuracy on a pretrained model. To validate YOLOv5s-cls accuracy on ImageNet.
+```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
+```
+
+### Predict
+Run a classification prediction on an image.
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
+```
+```python
+model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load from PyTorch Hub
+```
+
+### Export
+Export a group of trained YOLOv5-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
+```
+</details>
+
+
 ## <div align="center">贡献</div>
 
 我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!

.github/workflows/ci-testing.yml

@@ -5,9 +5,9 @@ name: YOLOv5 CI
 
 on:
   push:
-    branches: [master]
+    branches: [ master ]
   pull_request:
-    branches: [master]
+    branches: [ master ]
   schedule:
     - cron: '0 0 * * *'  # runs at 00:00 UTC every day
 
@@ -16,9 +16,9 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest]
-        python-version: ['3.9']  # requires python<=3.9
-        model: [yolov5n]
+        os: [ ubuntu-latest ]
+        python-version: [ '3.9' ]  # requires python<=3.9
+        model: [ yolov5n ]
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
@@ -47,9 +47,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ['3.10']
-        model: [yolov5n]
+        os: [ ubuntu-latest, macos-latest, windows-latest ]
+        python-version: [ '3.10' ]
+        model: [ yolov5n ]
         include:
           - os: ubuntu-latest
             python-version: '3.7'  # '3.6.8' min
@@ -87,7 +87,7 @@ jobs:
           else
               pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
           fi
-        shell: bash  # required for Windows compatibility
+        shell: bash  # for Windows compatibility
       - name: Check environment
         run: |
           python -c "import utils; utils.notebook_init()"
@@ -100,8 +100,8 @@ jobs:
           python --version
           pip --version
           pip list
-      - name: Run tests
-        shell: bash
+      - name: Test detection
+        shell: bash  # for Windows compatibility
         run: |
           # export PYTHONPATH="$PWD"  # to run '$ python *.py' files in subdirectories
           m=${{ matrix.model }}  # official weights
@@ -123,3 +123,13 @@ jobs:
               model = torch.hub.load('.', 'custom', path=path, source='local')
               print(model('data/images/bus.jpg'))
           EOF
+      - name: Test classification
+        shell: bash  # for Windows compatibility
+        run: |
+          m=${{ matrix.model }}-cls.pt  # official weights
+          b=runs/train-cls/exp/weights/best.pt  # best.pt checkpoint
+          python classify/train.py --imgsz 32 --model $m --data mnist2560 --epochs 1  # train
+          python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist2560  # val
+          python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist2560/test/7/60.png  # predict
+          python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg  # predict
+          python export.py --weights $b --img 64 --imgsz 224 --include torchscript  # export