MMDetection

Google Colaboratory - Image and Video with Faster R-CNN on Version 3.3.0
https://colab.research.google.com/drive/11pbaSUnA8eWopz2-OHh9XkCiTQdbMl8t

Google Colaboratory - MS-COCO Training with Faster R-CNN on Version 3.3.0
https://colab.research.google.com/drive/1tYssvTbCa0zuICyeVShUx42PGMeJxWlW

Google Colaboratory - Image and Video with Faster R-CNN/YOLOX/DETR/Mask R-CNN/Panoptic FPN/Mask2Former on Version 2.28.2
https://colab.research.google.com/drive/1hbOLBwOBeCVOELl3-I2yXD0QTmWKVylZ

https://mmdetection.readthedocs.io/en/latest/get_started.html
https://qiita.com/apiss/items/48475abc20abf26c0d27
https://qiita.com/fam_taro/items/7f028dfeae2a79a10fe1

The official approach is using OpenMIM and the latest repositories on GitHub but the following steps simply work on Google Colab:

!pip install mmengine
!pip install mmcv==2.1.0
!pip install mmdet

## 01. MMEngine is recommended to be installed
mim install mmengine

## 02. show_result_pyplot and model.show_result() have been removed (see below for alternative approaches)
#from mmdet.apis import show_result_pyplot

## 03. Register all modules
from mmdet.utils import register_all_modules
register_all_modules()

## 04. Some configuration file names have been changed
#mim download mmdet --config faster_rcnn_r50_fpn_1x_coco --dest $config_dir
mim download mmdet --config faster-rcnn_r50_fpn_1x_coco --dest $config_dir

https://github.com/open-mmlab/mmcv/issues/3059

## Downgrading PyTorch on Google Colab (2.3.0+cu121) for MMCV Compatibility (Confirmed 2024/07/04)
!pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
!pip install --upgrade openmim
!mim install 'mmcv==2.1.0'

## Resolving the dependency conflicts for openmim: ipython 7.9.0 requires jedi>=0.10
!pip install --upgrade jedi

https://github.com/open-mmlab/mmdetection/issues/10380

• MMEngine Visualizer is recommended to be used instead of show_result()
• Option A is the official approach, but font sizes might be too small if on a large image
• Option B is a tailor-made approach for visualization with annotations

## Loading Image
import mmcv
image = mmcv.imread(img, channel_order='rgb')
#image = mmcv.imconvert(image, 'bgr', 'rgb')

## Model and Result
model = init_detector(config_file, checkpoint_file, device=device)
result = inference_detector(model, img)

## Option A
from mmdet.registry import VISUALIZERS

visualizer = VISUALIZERS.build(model.cfg.visualizer)
visualizer.dataset_meta = model.dataset_meta
visualizer.add_datasample('result', image, data_sample=result, draw_gt=False, show=True, wait_time=0, pred_score_thr=0.70, out_file='result.jpg')
visualizer.get_image()
visualizer.show()

## Option B
import torch
import cv2
from mmengine.visualization import Visualizer

pred_score_thr = 0.70
scores = result.pred_instances.scores[torch.where(result.pred_instances.scores>=pred_score_thr)]
labels = result.pred_instances.labels[torch.where(result.pred_instances.scores>=pred_score_thr)]
bboxes = result.pred_instances.bboxes[torch.where(result.pred_instances.scores>=pred_score_thr)]
labels_classes = list(map(lambda x: model.dataset_meta['classes'][x], labels.tolist()))
labels_palette = list(map(lambda x: model.dataset_meta['palette'][x], labels.tolist()))
labels_print = list(map(lambda x, y: f'{x}: {(y * 100.0):.1f}', labels_classes, scores.tolist()))
bboxes_origins = list(map(lambda x: [x[0], x[1]], bboxes.tolist()))
visualizer = Visualizer(image=image)
visualizer.draw_bboxes(bboxes, edge_colors=labels_palette, line_widths=3)
visualizer.draw_texts(labels_print, torch.tensor(bboxes_origins), font_sizes=30, colors='white', bboxes=dict(facecolor='red', edgecolor='black', linewidth=0, alpha=0.8))
visualizer.get_image()
visualizer.show()
cv2.imwrite('result.jpg', cv2.cvtColor(visualizer.get_image(), cv2.COLOR_RGB2BGR))

https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html
https://mmdetection.readthedocs.io/en/latest/user_guides/train.html

https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/losses/__init__.py

https://pytorch.org/docs/stable/optim.html

optimizer=dict(_delete_=True) removes originally-defined parameters in dict()

https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/backbones/resnet.py

• frozen_stages = -1 : not freezing any parameters
• frozen_stages = 0 : freezing the stem part only
• frozen_stages = n [n >= 1] : freezing the stem part and first n stages

https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md/
https://mmdetection.readthedocs.io/en/latest/get_started.html
https://mmdetection.readthedocs.io/en/latest/1_exist_data_model.html
https://dev.classmethod.jp/articles/mmdetection-detect-samples/
https://github.com/open-mmlab/mmcv/issues/3059
https://github.com/open-mmlab/mmdetection/issues/10380
https://qiita.com/apiss/items/48475abc20abf26c0d27
https://qiita.com/fam_taro/items/7f028dfeae2a79a10fe1
https://qiita.com/saliton/items/24b69d1fa274b21c489a
https://qiita.com/sister_DB/items/42560f551b65dd976217

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