[Docs] Add an ionogram example in MMYOLO application (#643)

* [Fix] fix the config link for rtmdet_tiny

* fir the lint

* upload configs

* [Docs]Add doc for application examples

* [Doc] fix h1 title for ionogram_detection.md

* [Fix] update doc links for project/misc/

* Update links for application_examples in README

* Update links for application_examples in README

* Fix the bad commit caused by a mistake merge.

* Fix unnecessary modification on Table of Contents

* Add description of dataset preparation

* Add description of dataset preparation

* Update dataset analysis and pipeline visualization

* fix line too long

* Update rtmdet_s in the benchmark

* fix typo in the doc

* Replace local ckpt path with url

* Update file tree, check out for spaces

* Beautify configs

* Beautify configs

* Add introduction of annotation tool

* Update rtmdet benchmark

* Update rtmdet configs

* Fix typo

* Add English doc

* Minor revisio

* Fix typo

* Revision - check labels

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - sentence structure

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - fix typo

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - parameters and FLOPs

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - replace flops with FLOPs

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - enhance readability

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - fix typo

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - correct grammatical errors

Co-authored-by: Range King <RangeKingHZ@gmail.com>

* Revision - enhance

Co-authored-by: Range King <RangeKingHZ@gmail.com>

---------

Co-authored-by: Range King <RangeKingHZ@gmail.com>

README.md

@@ -192,7 +192,7 @@ For different parts from MMDetection, we have also prepared user guides and adva
 - [Visualization](docs/en/recommended_topics/visualization.md)
 - [Model deployment](docs/en/recommended_topics/deploy/)
 - [Troubleshooting steps](docs/en/recommended_topics/troubleshooting_steps.md)
-- [MMYOLO industry examples](docs/en/recommended_topics/industry_examples.md)
+- [MMYOLO application examples](docs/en/recommended_topics/application_examples/)
 - [MM series repo essential basics](docs/en/recommended_topics/mm_basics.md)
 - [Dataset preparation and description](docs/en/recommended_topics/dataset_preparation.md)
 

README_zh-CN.md

@@ -213,7 +213,7 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
 - [关于可视化的一切](docs/zh_cn/recommended_topics/visualization.md)
 - [模型部署流程](docs/zh_cn/recommended_topics/deploy/)
 - [常见错误排查步骤](docs/zh_cn/recommended_topics/troubleshooting_steps.md)
-- [MMYOLO 产业范例介绍](docs/zh_cn/recommended_topics/industry_examples.md)
+- [MMYOLO 应用范例介绍](docs/zh_cn/recommended_topics/application_examples/)
 - [MM 系列 Repo 必备基础](docs/zh_cn/recommended_topics/mm_basics.md)
 - [数据集准备和说明](docs/zh_cn/recommended_topics/dataset_preparation.md)
 

docs/en/index.rst

@@ -21,13 +21,13 @@ You can switch between Chinese and English documents in the top-right corner of
    recommended_topics/contributing.md
    recommended_topics/model_design.md
    recommended_topics/algorithm_descriptions/index.rst
+   recommended_topics/application_examples/index.rst
    recommended_topics/replace_backbone.md
    recommended_topics/complexity_analysis.md
    recommended_topics/labeling_to_deployment_tutorials.md
    recommended_topics/visualization.md
    recommended_topics/deploy/index.rst
    recommended_topics/troubleshooting_steps.md
-   recommended_topics/industry_examples.md
    recommended_topics/mm_basics.md
    recommended_topics/dataset_preparation.md
 

docs/en/recommended_topics/application_examples/index.rst

@@ -0,0 +1,7 @@
+MMYOLO application examples
+********************
+
+.. toctree::
+   :maxdepth: 1
+
+   ionogram_detection.md

docs/zh_cn/index.rst

@@ -21,13 +21,13 @@
    recommended_topics/contributing.md
    recommended_topics/model_design.md
    recommended_topics/algorithm_descriptions/index.rst
+   recommended_topics/application_examples/index.rst
    recommended_topics/replace_backbone.md
    recommended_topics/complexity_analysis.md
    recommended_topics/labeling_to_deployment_tutorials.md
    recommended_topics/visualization.md
    recommended_topics/deploy/index.rst
    recommended_topics/troubleshooting_steps.md
-   recommended_topics/industry_examples.md
    recommended_topics/mm_basics.md
    recommended_topics/dataset_preparation.md
 

docs/zh_cn/recommended_topics/application_examples/index.rst

@@ -0,0 +1,7 @@
+MMYOLO 应用范例介绍
+********************
+
+.. toctree::
+   :maxdepth: 1
+
+   ionogram_detection.md

projects/misc/custom_dataset/README.md

@@ -1,3 +1,3 @@
-Tips: 这个是自定义数据集的 config 文件，请结合 [自定义数据集教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md) 来使用。
+Tips: 这个是自定义数据集的 config 文件，请结合 [标注+训练+测试+部署全流程](https://github.com/open-mmlab/mmyolo/blob/main/docs/zh_cn/recommended_topics/labeling_to_deployment_tutorials.md) 来使用。
 
-Tips: This is the config file of the custom dataset. Please use it in combination with [custom dataset](https://github.com/open-mmlab/mmyolo/blob/dev/docs/en/user_guides/custom_dataset.md).
+Tips: This is the config file of the custom dataset. Please use it in combination with [Annotation-to-deployment workflow for custom dataset](https://github.com/open-mmlab/mmyolo/blob/main/docs/en/recommended_topics/labeling_to_deployment_tutorials.md).

projects/misc/ionogram_detection/README.md

@@ -0,0 +1,3 @@
+Tips: 这是 MMYOLO 应用范例的配置文件，请结合 [基于 MMYOLO 的频高图实时目标检测 benchmark](/docs/zh_cn/recommended_topics/application_examples/ionogram_detection.md) 来使用。
+
+Tips: This is the config file of the MMYOLO application examples. Please use it in combination with [A Benchmark for Ionogram Detection Based on MMYOLO](/docs/en/recommended_topics/application_examples/ionogram_detection.md).

projects/misc/ionogram_detection/rtmdet/rtmdet_l_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,107 @@
+_base_ = 'mmyolo::rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
+
+# ======================== Modified parameters ======================
+# -----data related-----
+data_root = './Iono4311/'
+train_ann_file = 'annotations/train.json'
+train_data_prefix = 'train_images/'
+val_ann_file = 'annotations/val.json'
+val_data_prefix = 'val_images/'
+test_ann_file = 'annotations/test.json'
+test_data_prefix = 'test_images/'
+
+class_name = ('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F')
+num_classes = len(class_name)
+metainfo = dict(
+    classes=class_name,
+    palette=[(250, 165, 30), (120, 69, 125), (53, 125, 34), (0, 11, 123),
+             (130, 20, 12), (120, 121, 80)])
+
+train_batch_size_per_gpu = 32
+train_num_workers = 8
+val_batch_size_per_gpu = train_batch_size_per_gpu
+
+# Config of batch shapes. Only on val.
+batch_shapes_cfg = dict(batch_size=val_batch_size_per_gpu)
+
+# -----train val related-----
+load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco/rtmdet_l_syncbn_fast_8xb32-300e_coco_20230102_135928-ee3abdc4.pth'  # noqa
+
+# default hooks
+save_epoch_intervals = 10
+max_epochs = 100
+max_keep_ckpts = 1
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0,
+        end=300),
+    dict(
+        # use cosine lr from 20 to 100 epoch
+        type='CosineAnnealingLR',
+        eta_min=_base_.base_lr * 0.05,
+        begin=max_epochs // 5,
+        end=max_epochs,
+        T_max=max_epochs * 4 // 5,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# train_cfg
+val_interval = 2
+val_begin = 20
+
+tta_model = None
+tta_pipeline = None
+
+visualizer = dict(
+    vis_backends=[dict(type='LocalVisBackend'),
+                  dict(type='WandbVisBackend')])
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    bbox_head=dict(head_module=dict(num_classes=num_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_classes)))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=train_ann_file,
+        data_prefix=dict(img=train_data_prefix)))
+
+val_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        data_prefix=dict(img=val_data_prefix),
+        ann_file=val_ann_file))
+
+test_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        data_prefix=dict(img=test_data_prefix),
+        ann_file=test_ann_file))
+
+default_hooks = dict(
+    checkpoint=dict(
+        interval=save_epoch_intervals,
+        max_keep_ckpts=max_keep_ckpts,
+        save_best='auto'))
+
+val_evaluator = dict(ann_file=data_root + val_ann_file)
+test_evaluator = dict(ann_file=data_root + test_ann_file)
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_begin=val_begin,
+    val_interval=val_interval)

projects/misc/ionogram_detection/rtmdet/rtmdet_s_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,83 @@
+_base_ = './rtmdet_l_fast_1xb32-100e_ionogram.py'
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco/rtmdet_s_syncbn_fast_8xb32-300e_coco_20221230_182329-0a8c901a.pth'  # noqa
+
+# ======================= Modified parameters =====================
+deepen_factor = 0.33
+widen_factor = 0.5
+img_scale = _base_.img_scale
+
+# ratio range for random resize
+random_resize_ratio_range = (0.5, 2.0)
+# Number of cached images in mosaic
+mosaic_max_cached_images = 40
+# Number of cached images in mixup
+mixup_max_cached_images = 20
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        use_cached=True,
+        max_cached_images=mosaic_max_cached_images,
+        pad_val=114.0),
+    dict(
+        type='mmdet.RandomResize',
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=random_resize_ratio_range,  # note
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(type='mmdet.RandomCrop', crop_size=img_scale),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='YOLOv5MixUp',
+        use_cached=True,
+        max_cached_images=mixup_max_cached_images),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='mmdet.RandomResize',
+        scale=img_scale,
+        ratio_range=random_resize_ratio_range,  # note
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(type='mmdet.RandomCrop', crop_size=img_scale),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=_base_.max_epochs - _base_.num_epochs_stage2,
+        switch_pipeline=train_pipeline_stage2)
+]

projects/misc/ionogram_detection/rtmdet/rtmdet_tiny_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,62 @@
+_base_ = './rtmdet_s_fast_1xb32-100e_ionogram.py'
+
+# ======================= Modified parameters ======================
+deepen_factor = 0.167
+widen_factor = 0.375
+img_scale = _base_.img_scale
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco/rtmdet_tiny_syncbn_fast_8xb32-300e_coco_20230102_140117-dbb1dc83.pth'  # noqa
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0,
+        end=300),
+    dict(
+        # use cosine lr from 50 to 100 epoch
+        type='CosineAnnealingLR',
+        eta_min=_base_.base_lr * 0.05,
+        begin=_base_.max_epochs // 2,
+        end=_base_.max_epochs,
+        T_max=_base_.max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        use_cached=True,
+        max_cached_images=20,  # note
+        random_pop=False,  # note
+        pad_val=114.0),
+    dict(
+        type='mmdet.RandomResize',
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=(0.5, 2.0),
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(type='mmdet.RandomCrop', crop_size=img_scale),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='YOLOv5MixUp',
+        use_cached=True,
+        random_pop=False,
+        max_cached_images=10,
+        prob=0.5),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))