[Docs] Translate yolov5_tutorial.md (open-mmlab#138)

* [Feature] Make docker image smaller (open-mmlab#67)

* [Feature] Make docker image smaller

* Improve doc

* Improve doc

* Improve doc

* Improve doc

* Use Compose directly in BaseMixImageTransform (open-mmlab#71)

* Use 'Compose' directly

Use 'Compose' directly

* avoids some unnecessary calculations

avoids some unnecessary calculations.

* remove whitespace

remove whitespace

* yolov5_description.md eng draft 1

* Update docs/en/user_guides/yolov5_tutorial.md

Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>

* Update docs/en/user_guides/yolov5_tutorial.md

Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>

* yolov5_tutorial.md translation draft 2 lint fixed

* Update docs/en/user_guides/yolov5_tutorial.md

Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>

* Update docs/en/user_guides/yolov5_tutorial.md

Co-authored-by: cydiachen <cydiachencc@icloud.com>

* yolov5 tutorial draft 3

* yolov5 tutorial draft 4

* add a link on overview.md

Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>
Co-authored-by: jason_w <wongzheng@126.com>
Co-authored-by: cydiachen <cydiachencc@icloud.com>

docs/en/overview.md

@@ -31,7 +31,7 @@ MMYOLO file structure is identical to the MMDetection. To fully reuse the MMDete
 
 The detailed instruction of MMYOLO is as follows.
 
-1. Look up install instructions to get_started.md).
+1. Look up install instructions to [get_started.md](get_started.md).
 
 2. The basic method of how to use MMYOLO can be found here:
 

docs/en/user_guides/yolov5_tutorial.md

@@ -1 +1,248 @@
-# From Getting Started to Deployment tutorial with YOLOv5
+# From getting started to deployment with YOLOv5
+
+## Environment Setup
+
+Note: Since this repository uses OpenMMLab 2.0, please create a new conda virtual environment to prevent conflicts with your existing repositories and projects of OpenMMLab 1.0.
+
+```shell
+conda create -n open-mmlab python=3.8 -y
+conda activate open-mmlab
+conda install pytorch torchvision -c pytorch
+# conda install pytorch torchvision cpuonly -c pytorch
+pip install -U openmim
+mim install mmengine
+mim install "mmcv>=2.0.0rc1,<2.1.0"
+mim install "mmdet>=3.0.0rc0,<3.1.0"
+# for albumentations
+git clone https://github.com/open-mmlab/mmyolo.git
+cd mmyolo
+# Install albumentations
+pip install -r requirements/albu.txt
+# Install MMYOLO
+mim install -v -e .
+# "-v" means verbose, or more output
+# "-e" means install the project in editable mode, so any local modifications made to the code will take effect, eliminating the need to reinstall.
+```
+
+For more detailed information about environment configuration, please refer to [get_started](../get_started.md).
+
+## Dataset Preparation
+
+In this tutorial, we provide the ballon dataset, which is less than 40MB, as the training dataset for MMYOLO.
+
+```shell
+python tools/misc/download_dataset.py --dataset-name balloon --save-dir data --unzip
+python tools/dataset_converters/balloon2coco.py
+```
+
+After executing the above command, the balloon dataset will be downloaded in the `data` folder with the converted format we need. The `train.json` and `val.json` are the annotation files in the COCO format.
+
+<div align=center>
+<img src="https://cdn.vansin.top/img/20220912105312.png" alt="image"/>
+</div>
+
+## Config File Preparation
+
+Create a new file called the `yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py` configuration file in the `configs/yolov5` folder, and copy the following content into it.
+
+```python
+_base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
+
+data_root = 'data/balloon/'
+
+train_batch_size_per_gpu = 4
+train_num_workers = 2
+
+metainfo = {
+    'CLASSES': ('balloon', ),
+    'PALETTE': [
+        (220, 20, 60),
+    ]
+}
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        data_root=data_root,
+        metainfo=metainfo,
+        data_prefix=dict(img='train/'),
+        ann_file='train.json'))
+
+val_dataloader = dict(
+    dataset=dict(
+        data_root=data_root,
+        metainfo=metainfo,
+        data_prefix=dict(img='val/'),
+        ann_file='val.json'))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'val.json')
+
+test_evaluator = val_evaluator
+
+model = dict(bbox_head=dict(head_module=dict(num_classes=1)))
+
+default_hooks = dict(logger=dict(interval=1))
+```
+
+The above configuration is inherited from `./yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py`, and `data_root`, `metainfo`, `train_dataloader`, `val_dataloader`, `num_classes` and other configurations are updated according to the balloon data we are using.
+The reason why we set the `interval` of the logger to 1 is that the balloon data set we choose is relatively small, and if the `interval` is too large, we will not see the output of the loss-related log. Therefore, by setting the `interval` of the logger to 1 will ensure that each interval iteration will output a loss-related log.
+
+## Training
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
+```
+
+After executing the above training command, the `work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon` folder will be automatically generated. Both the weight and the training configuration files will be saved in this folder.
+
+<div align=center>
+<img src="https://cdn.vansin.top/img/20220913213846.png" alt="image"/>
+</div>
+
+### Resume training after interruptions
+
+If training stops midway, add `--resume` at the end of the training command, and the program will automatically load the latest weight file from `work_dirs` to resume training.
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py --resume
+```
+
+### Fine-tune with loaded pretrained weights
+
+NOTICE: It is highly recommended that finetuning from large datasets, such as COCO, can significantly boost the performance of overall network.
+In this example, compared with training from scratch, finetuning the pretrained model outperforms with a significant margin. (Over 30+ mAP boost than training from scratch).
+
+1. Download the COCO dataset pre-trained weights
+
+```shell
+cd mmyolo
+wget https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth
+```
+
+2. Load the pretrained model to train
+
+```shell
+cd mmyolo
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py \
+                      --cfg-options load_from='yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' custom_hooks=None
+```
+
+Note: Ideally, the `strict_load` initialization parameter of `EMAHook` should be set to `False` during the fine-tuning phase, which makes the command `custom_hooks.0.strict_load=False`. However, because MMEngine v0.1.0 is the initial development version, there will be problems with this setting right now. Therefore, for the time being, you can only use the command `custom_hooks=None` to turn off `custom_hooks` to load the pre-training weights correctly. This issue is expected to be fixed in the next release.
+
+3. Freeze backbone to train
+
+Freeze the four backbone stages by setting `model.backbone.frozen_stages=4` in the config file or from the command line.
+
+```shell
+# Set model.backbone.frozen_stages=4 from the command line
+cd mmyolo
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py \
+                      --cfg-options load_from='yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' model.backbone.frozen_stages=4 custom_hooks=None
+```
+
+### Visualization
+
+For `visualization` of `default_hooks` in `configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py`, we set `draw` to `True` and `interval` to `2`.
+
+```python
+default_hooks = dict(
+     logger=dict(interval=1),
+     visualization=dict(draw=True, interval=2),
+)
+```
+
+Re-run the following training command. During the validation, each `interval` image will save a puzzle of the annotation and prediction results to `work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon/{timestamp}/vis_data/vis_image` folder.
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
+```
+
+<div align=center>
+<img src="https://moonstarimg.oss-cn-hangzhou.aliyuncs.com/img/20220920094007.png" alt="image"/>
+</div>
+
+#### Visualization Backend Support
+
+MMEngine supports various backends such as local, TensorBoard, and wandb.
+
+- wandb
+
+Register and get your wandb API key from the [official website](https://wandb.ai/settings).
+
+<div align=center>
+<img src="https://cdn.vansin.top/img/20220913212628.png" alt="image"/>
+</div>
+
+```shell
+pip install wandb
+
+wandb login
+# enter your API key, then you can see if you login successfully
+```
+
+Add wandb configuration in `configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py`.
+
+```python
+visualizer = dict(vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')])
+```
+
+Re-run the training command to check data visualization results such as loss, learning rate, and coco/bbox_mAP in the web link prompted on the command line.
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
+```
+
+<div align=center>
+<img src="https://cdn.vansin.top/img/20220913213221.png" alt="image"/>
+</div>
+
+- Tensorboard
+
+Install Tensorboard
+
+```shell
+pip install tensorboard
+```
+
+Similar to wandb, we need to add Tensorboard configuration in `configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py`.
+
+```python
+visualizer = dict(vis_backends=[dict(type='LocalVisBackend'),dict(type='TensorboardVisBackend')])
+```
+
+Re-run the training command, a Tensorboard folder will be created in `work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon/{timestamp}/vis_data`, You can get data visualization results such as loss, learning rate, and coco/bbox_mAP in the web link prompted on the command line with the following command:
+
+```shell
+tensorboard --logdir=work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon
+```
+
+## Model Testing
+
+If you set `custom_hooks=None` during training, you still need to set `custom_hooks=None` during model testing as well.
+
+```shell
+python tools/test.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py \
+                      work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon/epoch_300.pth \
+                      --show-dir show_results --cfg-options custom_hooks=None
+```
+
+If you don't set `custom_hooks=None` during training, the test command should be as follows:
+
+```shell
+python tools/test.py configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py \
+                      work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon/epoch_300.pth \
+                      --show-dir show_results
+```
+
+Run the above command, the inference result picture will be automatically saved to the `work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon/{timestamp}/show_results` folder. The following is one of the result pictures. The left one is the actual annotation, and the right is the model inference result.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/27466624/190913272-f99709e5-c798-46b8-aede-30f4e91683a3.jpg" alt="result_img"/>
+</div>
+
+## Model Deployment
+
+Under development...

docs/zh_cn/user_guides/yolov5_tutorial.md

@@ -85,7 +85,6 @@ test_evaluator = val_evaluator
 model = dict(bbox_head=dict(head_module=dict(num_classes=1)))
 
 default_hooks = dict(logger=dict(interval=1))
-
 ```
 
 以上配置从 `./yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py` 中继承，并根据 balloon 数据的特点更新了 `data_root`、`metainfo`、`train_dataloader`、`val_dataloader`、`num_classes` 等配置。