[doc + live-code + yolov5] update dockerfile and readme

.gitignore

@@ -27,3 +27,5 @@ yolov4_training/yolov4.conv.137
 yolov4_training/build_docker.sh
 yolov4_training/dockerfile_tmp
 yolov4_training/yolov4.conv.137
+det-demo-tmi/voc_dog
+site/

.readthedocs.yaml

@@ -0,0 +1,20 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+
+mkdocs:
+  configuration: mkdocs.yml
+
+# Optionally declare the Python requirements required to build your docs
+python:
+   install:
+   - requirements: docs/requirements.txt

README.md

@@ -0,0 +1,80 @@
+# ymir-executor documentation [English](./README.md) | [简体中文](./README_zh-CN.md)
+
+-  🏠 [ymir](https://github.com/IndustryEssentials/ymir)
+
+-  📺 [video tutorial](https://b23.tv/KS5b5oF)
+
+-  👨‍👩‍👧‍👧 [Image Community](http://pubimg.vesionbook.com:8110/img) search and share open source.
+
+-  📘 [Documence](https://ymir-executor-fork.readthedocs.io/zh/latest/#)
+
+## overview
+
+| docker image | [finetune](https://github.com/modelai/ymir-executor-fork/wiki/use-yolov5-to-finetune-or-training-model) | tensorboard | args/cfg options | framework | onnx | pretrained weights |
+| - | - | - | - | - | - | - |
+| yolov4 | ? | ✔️ | ❌ | darknet + mxnet | ❌ | local |
+| yolov5 | ✔️ | ✔️ | ✔️ | pytorch | ✔️ | local+online |
+| yolov7 | ✔️ | ✔️ | ✔️ | pytorch | ❌ | local+online |
+| mmdetection | ✔️ | ✔️ | ✔️ | pytorch | ❌ | local+online |
+| detectron2 | ✔️ | ✔️ | ✔️ | pytorch | ❌ | online |
+| vidt | ? | ✔️ | ✔️ | pytorch | ❌ | online |
+| nanodet | ✔️ | ✔️ | ❌ | pytorch_lightning | ❌ | local+online |
+
+- `online` pretrained weights may download through network
+
+- `local` pretrained weights have copied to docker images when building image
+
+### benchmark
+
+- training dataset: voc2012-train 5717 images
+- validation dataset: voc2012-val 5823 images
+- image size: 640
+
+gpu: single Tesla P4
+
+| docker image | batch size | epoch number | model | voc2012 val map50 | training time | note |
+| - | - | - | - | - | - | - |
+| yolov5 | 16 | 100 | yolov5s | 70.05% | 9h | coco-pretrained |
+| vidt | 2 | 100 | swin-nano | 54.13% | 2d | imagenet-pretrained |
+| yolov4 | 4 | 20000 steps | yolov4 | 66.18% | 2d | imagenet-pretrained |
+| yolov7 | 16 | 100 | yolov7-tiny | 70% | 8h | coco-pretrained |
+
+gpu: single GeForce GTX 1080 Ti
+
+| docker image | image size | batch size | epoch number | model | voc2012 val map50 | training time | note |
+| - | - | - | - | - | - | - | - |
+| yolov4 | 608 | 64/32 | 20000 steps | yolov4 | 72.73% | 6h | imagenet-pretrained |
+| yolov5 | 640 | 16 | 100 | yolov5s | 70.35% | 2h | coco-pretrained |
+| yolov7 | 640 | 16 | 100 | yolov7-tiny | 70.4% | 5h | coco-pretrained |
+| mmdetection | 640 | 16 | 100 | yolox_tiny | 66.2% | 5h | coco-pretrained |
+| detectron2 | 640 | 2 | 20000 steps | retinanet_R_50_FPN_1x | 53.54% | 2h | imagenet-pretrained |
+| nanodet | 416 | 16 | 100 | nanodet-plus-m_416 | 58.63% | 5h | imagenet-pretrained |
+
+---
+
+## how to import pretrained model weights
+
+- [import and finetune model](https://github.com/modelai/ymir-executor-fork/wiki/import-and-finetune-model)
+
+- [import pretainted model weights](https://github.com/IndustryEssentials/ymir/blob/master/dev_docs/import-extra-models.md)
+
+## reference
+
+### object detection
+- [ymir-yolov5](https://github.com/modelai/ymir-yolov5)
+- [ymir-yolov7](https://github.com/modelai/ymir-yolov7)
+- [ymir-nanodet](https://github.com/modelai/ymir-nanodet)
+- [ymir-mmyolo](https://github.com/modelai/ymir-mmyolo)
+- [ymir-vidt](https://github.com/modelai/ymir-vidt)
+- [ymir-detectron2](https://github.com/modelai/ymir-detectron2)
+
+### semantic segmenation
+- [ymir-mmsegmentation](https://github.com/modelai/ymir-mmsegmentation)
+
+### instance segmentation
+- [ymir-yolov5-seg](https://github.com/modelai/ymir-yolov5-seg)
+
+### resource
+- [ymir-executor-sdk](https://github.com/modelai/ymir-executor-sdk) ymir_exc package, help to develop your image
+- [ymir-executor-verifier](https://github.com/modelai/ymir-executor-verifier) test your ymir image
+- [ymir-flask](https://github.com/modelai/ymir-flask) deploy your model on website

README_zh-CN.md

@@ -0,0 +1,84 @@
+# ymir-executor 使用文档 [English](./README.md) | [简体中文](./README_zh-CN.md)
+
+- 🏠 [ymir](https://github.com/IndustryEssentials/ymir)
+
+- 📺 [视频教程](https://b23.tv/KS5b5oF)
+
+- 👨‍👩‍👧‍👧 [镜像社区](http://pubimg.vesionbook.com:8110/img) 可搜索到所有公开的ymir算法镜像， 同时可共享其他人发布的镜像。
+
+- 📘 [文档](https://ymir-executor-fork.readthedocs.io/zh/latest/#)
+
+## 比较
+
+| docker image | [finetune](https://github.com/modelai/ymir-executor-fork/wiki/use-yolov5-to-finetune-or-training-model) | tensorboard | args/cfg options | framework | onnx | pretrained weight |
+| - | - | - | - | - | - | - |
+| yolov4 | ? | ✔️ | ❌ | darknet + mxnet | ❌ | local |
+| yolov5 | ✔️ | ✔️ | ✔️ | pytorch | ✔️ | local+online |
+| yolov7 | ✔️ | ✔️ | ✔️ | pytorch | ❌ | local+online |
+| mmdetection | ✔️ | ✔️ | ✔️ | pytorch | ❌ | local+online |
+| detectron2 | ✔️ | ✔️ | ✔️ | pytorch | ❌ | online |
+| vidt | ? | ✔️ | ✔️ | pytorch | ❌ | online |
+| nanodet | ✔️ | ✔️ | ❌ | pytorch_lightning | ❌ | local+online |
+
+- `online` 预训练权重可能在训练时通过网络下载
+
+- `local` 预训练权重在构建镜像时复制到了镜像
+
+### benchmark
+
+- 训练集: voc2012-train 5717 images
+- 测试集: voc2012-val 5823 images
+- 图像大小: 640 (nanodet为416, yolov4为608)
+
+**由于 coco 数据集包含 voc 数据集中的类, 因此这个对比并不公平, 仅供参考**
+
+gpu: single Tesla P4
+
+| docker image | batch size | epoch number | model | voc2012 val map50 | training time | note |
+| - | - | - | - | - | - | - |
+| yolov5 | 16 | 100 | yolov5s | 70.05% | 9h | coco-pretrained |
+| vidt | 2 | 100 | swin-nano | 54.13% | 2d | imagenet-pretrained |
+| yolov4 | 4 | 20000 steps | yolov4 | 66.18% | 2d | imagenet-pretrained |
+| yolov7 | 16 | 100 | yolov7-tiny | 70% | 8h | coco-pretrained |
+
+gpu: single GeForce GTX 1080 Ti
+
+| docker image | image size | batch size | epoch number | model | voc2012 val map50 | training time | note |
+| - | - | - | - | - | - | - | - |
+| yolov4 | 608 | 64/32 | 20000 steps | yolov4 | 72.73% | 6h | imagenet-pretrained |
+| yolov5 | 640 | 16 | 100 | yolov5s | 70.35% | 2h | coco-pretrained |
+| yolov7 | 640 | 16 | 100 | yolov7-tiny | 70.4% | 5h | coco-pretrained |
+| mmdetection | 640 | 16 | 100 | yolox_tiny | 66.2% | 5h | coco-pretrained |
+| detectron2 | 640 | 2 | 20000 steps | retinanet_R_50_FPN_1x | 53.54% | 2h | imagenet-pretrained |
+| nanodet | 416 | 16 | 100 | nanodet-plus-m_416 | 58.63% | 5h | imagenet-pretrained |
+
+---
+
+## 如何导入预训练模型
+
+- [如何导入并精调外部模型](https://github.com/modelai/ymir-executor-fork/wiki/import-and-finetune-model)
+
+- [如何导入外部模型](https://github.com/IndustryEssentials/ymir/blob/master/dev_docs/import-extra-models.md)
+
+    - 通过ymir网页端的 `模型管理/模型列表/导入模型` 同样可以导入模型
+
+## 参考
+
+### 目标检测
+- [ymir-yolov5](https://github.com/modelai/ymir-yolov5)
+- [ymir-yolov7](https://github.com/modelai/ymir-yolov7)
+- [ymir-nanodet](https://github.com/modelai/ymir-nanodet)
+- [ymir-mmyolo](https://github.com/modelai/ymir-mmyolo)
+- [ymir-vidt](https://github.com/modelai/ymir-vidt)
+- [ymir-detectron2](https://github.com/modelai/ymir-detectron2)
+
+### 语义分割
+- [ymir-mmsegmentation](https://github.com/modelai/ymir-mmsegmentation)
+
+### 实例分割
+- [ymir-yolov5-seg](https://github.com/modelai/ymir-yolov5-seg)
+
+### 资源
+- [ymir-executor-sdk](https://github.com/modelai/ymir-executor-sdk) ymir_exc 包，辅助开发镜像
+- [ymir-executor-verifier](https://github.com/modelai/ymir-executor-verifier) 测试镜像工具
+- [ymir-flask](https://github.com/modelai/ymir-flask) 云端部署示例

det-demo-tmi/Dockerfile

@@ -0,0 +1,30 @@
+# a docker file for an sample training / mining / infer executor
+
+FROM python:3.8.13-alpine
+
+RUN sed -i 's/dl-cdn.alpinelinux.org/mirrors.tuna.tsinghua.edu.cn/g' /etc/apk/repositories
+# Add bash
+RUN apk add bash
+# Required to build numpy wheel
+RUN apk add g++ git make
+
+COPY requirements.txt ./
+RUN pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+WORKDIR /app
+# copy user code to WORKDIR
+COPY ./app/start.py /app/
+
+# copy user config template and manifest.yaml to /img-man
+RUN mkdir -p /img-man
+COPY img-man/*.yaml /img-man/
+
+# view https://github.com/protocolbuffers/protobuf/issues/10051 for detail
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+# entry point for your app
+# the whole docker image will be started with `nvidia-docker run <other options> <docker-image-name>`
+# and this command will run automatically
+
+RUN echo "python /app/start.py" > /usr/bin/start.sh
+CMD bash /usr/bin/start.sh