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@ -15,11 +15,15 @@ Take the small dataset of cat as an example, you can easily learn MMYOLO object
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- [Testing](#testing)
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- [EasyDeploy](#easydeploy-deployment)
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In this article, we take YOLOv5-s as an example. For the rest of the YOLO series algorithms, please see the corresponding algorithm configuration folder.
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In this tutorial, we take YOLOv5-s as an example. For the rest of the YOLO series algorithms, please see the corresponding algorithm configuration folder.
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## Installation
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Assuming you've already installed Conda in advance, install PyTorch
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Assuming you've already installed Conda in advance, then install PyTorch using the following commands.
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```{note}
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Note: Since this repo uses OpenMMLab 2.0, it is better to create a new conda virtual environment to prevent conflicts with the repo installed in OpenMMLab 1.0.
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```
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```shell
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conda create -n mmyolo python=3.8 -y
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@ -30,7 +34,7 @@ conda install pytorch torchvision -c pytorch
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# conda install pytorch torchvision cpuonly -c pytorch
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```
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Install MMYOLO and dependency libraries
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Install MMYOLO and dependency libraries using the following commands.
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```shell
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git clone https://github.com/open-mmlab/mmyolo.git
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@ -46,11 +50,7 @@ mim install -v -e .
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# thus any local modifications made to the code will take effect without reinstallation.
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```
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```{note}
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Note: Since this repo uses OpenMMLab 2.0, it is better to create a new conda virtual environment to prevent conflicts with the repo installed in OpenMMLab 1.0.
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```
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For details about how to configure the environment, see [Installation and verification](./installation.md)
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For details about how to configure the environment, see [Installation and verification](./installation.md).
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## Dataset
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@ -258,7 +258,7 @@ python tools/train.py configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py
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#### 2 Tensorboard
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Install Tensorboard environment
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Install Tensorboard package:
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```shell
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pip install tensorboard
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@ -274,7 +274,7 @@ After re-running the training command, Tensorboard file will be generated in the
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We can use Tensorboard to view the loss, learning rate, and coco/bbox_mAP visualizations from a web link by running the following command:
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```shell
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tensorboard --logdir=work_dirs/yolov5_s-v61_fast_1xb12-40e_cat.py
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tensorboard --logdir=work_dirs/yolov5_s-v61_fast_1xb12-40e_cat
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```
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## Testing
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@ -297,7 +297,7 @@ You can also visualize model inference results in a browser window if you use 'W
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MMYOLO provides visualization scripts for feature map to analyze the current model training. Please refer to [Feature Map Visualization](../recommended_topics/visualization.md)
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Due to the bias of direct visualization of `test_pipeline`, we need to `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` of `test_pipeline`
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Due to the bias of direct visualization of `test_pipeline`, we need to modify the `test_pipeline` of `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py`
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```python
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test_pipeline = [
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@ -318,7 +318,7 @@ test_pipeline = [
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]
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```
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modify to the following config:
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to the following config:
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```python
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test_pipeline = [
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@ -372,13 +372,19 @@ As can be seen from the above figure, because neck is involved in training, and
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Based on the above feature map visualization, we can analyze Grad CAM at the feature layer of bbox level.
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Install `grad-cam` package:
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```shell
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pip install "grad-cam"
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```
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(a) View Grad CAM of the minimum output feature map of the neck
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```shell
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python demo/boxam_vis_demo.py data/cat/images/IMG_20221020_112705.jpg \
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configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py \
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work_dirs/yolov5_s-v61_fast_1xb12-40e_cat/epoch_40.pth \
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--target-layer neck.out_layers[2]
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configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py \
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work_dirs/yolov5_s-v61_fast_1xb12-40e_cat/epoch_40.pth \
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--target-layer neck.out_layers[2]
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```
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<div align=center>
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@ -389,9 +395,9 @@ python demo/boxam_vis_demo.py data/cat/images/IMG_20221020_112705.jpg \
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```shell
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python demo/boxam_vis_demo.py data/cat/images/IMG_20221020_112705.jpg \
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configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py \
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work_dirs/yolov5_s-v61_fast_1xb12-40e_cat/epoch_40.pth \
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--target-layer neck.out_layers[1]
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configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py \
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work_dirs/yolov5_s-v61_fast_1xb12-40e_cat/epoch_40.pth \
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--target-layer neck.out_layers[1]
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```
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<div align=center>
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@ -402,9 +408,9 @@ python demo/boxam_vis_demo.py data/cat/images/IMG_20221020_112705.jpg \
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```shell
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python demo/boxam_vis_demo.py data/cat/images/IMG_20221020_112705.jpg \
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configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py \
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work_dirs/yolov5_s-v61_fast_1xb12-40e_cat/epoch_40.pth \
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--target-layer neck.out_layers[0]
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configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py \
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work_dirs/yolov5_s-v61_fast_1xb12-40e_cat/epoch_40.pth \
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--target-layer neck.out_layers[0]
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```
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<div align=center>
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@ -526,4 +532,4 @@ Here we choose to save the inference results under `output` instead of displayin
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This completes the transformation deployment of the trained model and checks the inference results. This is the end of the tutorial.
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The full content above can be viewed: [15_minutes_object_detection.ipynb](<>). If you encounter problems during training or testing, please check the \[common troubleshooting steps\](... /recommended_topics/troubleshooting_steps.md) first and feel free to raise an issue if you still can't solve it.
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The full content above can be viewed in [15_minutes_object_detection.ipynb](https://github.com/open-mmlab/mmyolo/blob/dev/demo/15_minutes_object_detection.ipynb). If you encounter problems during training or testing, please check the [common troubleshooting steps](../recommended_topics/troubleshooting_steps.md) first and feel free to open an [issue](https://github.com/open-mmlab/mmyolo/issues/new/choose) if you still can't solve it.
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@ -256,7 +256,7 @@ python tools/train.py configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py
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#### 2 Tensorboard 可视化使用
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安装 Tensorboard 环境
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安装 Tensorboard 依赖
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```shell
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pip install tensorboard
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@ -370,6 +370,12 @@ python demo/featmap_vis_demo.py data/cat/images/IMG_20221020_112705.jpg \
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基于上述特征图可视化效果，我们可以分析特征层 bbox 级别的 Grad CAM。
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安装 `grad-cam` 依赖：
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```shell
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pip install "grad-cam"
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```
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(a) 查看 neck 输出的最小输出特征图的 Grad CAM
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```shell
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@ -524,4 +530,4 @@ python projects/easydeploy/tools/image-demo.py \
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这样我们就完成了将训练完成的模型进行转换部署并且检查推理结果的工作。至此本教程结束。
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以上完整内容可以查看 [15_minutes_object_detection.ipynb](<>)。 如果你在训练或者测试过程中碰到问题，请先查看 [常见错误排除步骤](../recommended_topics/troubleshooting_steps.md)， 如果依然无法解决欢迎提 issue。
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以上完整内容可以查看 [15_minutes_object_detection.ipynb](https://github.com/open-mmlab/mmyolo/blob/dev/demo/15_minutes_object_detection.ipynb)。 如果你在训练或者测试过程中碰到问题，请先查看 [常见错误排除步骤](../recommended_topics/troubleshooting_steps.md)，如果依然无法解决欢迎提 [issue](https://github.com/open-mmlab/mmyolo/issues/new/choose)。
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