MMDetection starts fast and trains custom datasets

This article provides a quick guide to using MMDetection, documenting some of the issues to look out for in practice.

Environment to prepare

Based on the environment

• Nvidia graphics card host
• Ubuntu 18.04
  • System installation, visible production USB boot disk, and system installation
• Nvidia Driver
  • Driver installation, visible Ubuntu initial configuration – Nvidia driver

The development environment

Download and install Anaconda, and then run the following command in Terminal:

Create a Python virtual environmentConda create-n open-mmlab python=3.7 -y conda activate open-mmlabInstall PyTorch with CUDAConda install Pytorch ==1.6.0 TorchVision ==0.7.0 CudatoolKit =10.2 -C Pytorch -y# installation MMCVPIP install MMCV - full - f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html# installation MMDetection
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .
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Pytorch ==1.7.0 multi-card training issues, refer to this Issue. Command Reference:

Conda install Pytorch ==1.7.0 TorchVision ==0.8.1 cudatoolKit =10.2 -C Pytorch -y PIP install mmcV-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.0/index.htmlCopy the code

More installation methods, visible official documentation:

• MMDetection – Installation
• MMCV – Installation

Existing models are extrapolated

Faster RCNN

Using r-50-FPN as an example, download its model file to MMDetection /checkpoints/. And then, by inference,

conda activate open-mmlab

cd mmdetection/

python demo/image_demo.py \
demo/demo.jpg \
configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
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Test existing models

Preparing the data set

Download the COCO dataset and place it in the mmDetection /data/ COCO/directory as follows.

├─ data │ ├─ coco │ ├─ annotations │ ├─ train2017 │ ├─ val2017 │ ├─ test2017Copy the code

Testing existing models

cd mmdetection/

# single-gpu testing
python tools/test.py \
configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
--out results.pkl \
--eval bbox \
--show

# multi-gpu testing
bash tools/dist_test.sh \
configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
2 \
--out results.pkl \
--eval bbox
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The effect is as follows,

The results are as follows,

loading annotations into memory... Done (t = 0.33 s) creating the index... index created! [> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >] 5000/5000, 15.3 task/s, elapsed: 328 s, ETA: 0s writing results to results.pkl Evaluating bbox... Loading and preparing results... DONE (t = 0.89 s) creating the index... index created! Running per image evaluation... Evaluate annotationtype* Bbox * DONE (t=26.17s). Accumulating Evaluation Results with conditionals... DONE (t = 4.10 s). The Average Precision (AP) @ [IoU = 0.50:0.95 | area = all | maxDets = 100] = 0.374 Average Precision (AP) @ [ Ious = 0.50 | area = all | maxDets = 1000] = 0.581 Average Precision (AP) @ [IoU = 0.75 | area = all | maxDets = 1000] = 0.404 Average Precision (AP) @ [IoU = 0.50:0.95 | area = small | maxDets = 1000] = 0.212 Average Precision (AP) @ [IoU = 0.50:0.95 | Area = medium | maxDets = 1000] = 0.410 Average Precision (AP) @ [IoU = 0.50:0.95 | area = large | maxDets = 1000] = 0.481 Average Recall (AR) @ [IoU = 0.50:0.95 | area = all | maxDets = 100] = 0.517 Average Recall (AR) @ [IoU = 0.50:0.95 | area = All | maxDets = 300] = 0.517 Average Recall (AR) @ [IoU = 0.50:0.95 | area = all | maxDets = 1000] = 0.517 Average Recall (AR) @ [IoU = 0.50:0.95 | area = small | maxDets = 1000] = 0.326 Average Recall (AR) @ [IoU = 0.50:0.95 | area = medium | MaxDets = 1000] = 0.557 Average Recall (AR) @ [IoU = 0.50:0.95 | area = large | maxDets = 1000] = 0.648 OrderedDict ([('bbox_mAP', 0.374), ('bbox_mAP_50', 0.581), ('bbox_mAP_75', 0.404), ('bbox_mAP_s', 0.212), ('bbox_mAP_m', 0.41), ('bbox_mAP_l', 0.481), ('bbox_mAP_copypaste'.'0.374 0.581 0.404 0.212 0.410 0.481')])
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Standard data set training model

Preparing the data set

Same COCO dataset as in the previous section.

Preparing configuration Files

The configuration file is configs/ faster_rCNN/faster_rCNn_R50_fpn_1X_coco-.py.

You need to modify LR learning rate parameters based on your GPU as follows:

• Lr = 0.005 for 2 GPUs * 2 imgs/gpu
• Lr = 0.01 for 4 GPUs * 2 imgs/gpu
• Lr = 0.02 for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16), DEFAULT
• Lr = 0.08 for 16 GPUs * 4 imgs/gpu

_base_ = [
    '.. /_base_/models/faster_rcnn_r50_fpn.py'.'.. /_base_/datasets/coco_detection.py'.'.. /_base_/schedules/schedule_1x.py'.'.. /_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
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Training model

cd mmdetection/

# single-gpu training
python tools/train.py \
configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
--work-dir _train

# multi-gpu training
bash ./tools/dist_train.sh \
configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
2 \
--work-dir _train
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Custom data set training model

Custom data sets

Here to demonstrate cat as a custom dataset from the Pascal VOC dataset,

conda activate open-mmlab

# Dataset Management Framework (Datumaro)
pip install 'git+https://github.com/openvinotoolkit/datumaro'
# pip install tensorflow

datum convert --input-format voc --input-path ~/datasets/VOC2012 \
--output-format coco --output-dir ~/datasets/coco_voc2012_cat \
--filter '/item[annotation/label="cat"]'
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The data set needs to be in COCO format. Above, take CAT from VOC directly with datum and convert it to COCO format.

Preparing configuration Files

Add the configs/ VOC_cat/faster_rCNn_R50_fpn_1X_VOC_cat. py configuration file as follows:

# The new config inherits a base config to highlight the necessary modification
_base_ = [
    '.. /_base_/models/faster_rcnn_r50_fpn.py'.'.. /_base_/datasets/coco_detection.py'.'.. /_base_/schedules/schedule_1x.py'.'.. /_base_/default_runtime.py'
]

# We also need to change the num_classes in head to match the dataset's annotation
model = dict(
    roi_head=dict(
        bbox_head=dict(num_classes=1)))

# Modify dataset related settings
dataset_type = 'COCODataset'
classes = ('cat',)
data_root = '/home/john/datasets/'
data = dict(
    train=dict(
        img_prefix=data_root + 'VOC2012/JPEGImages/',
        classes=classes,
        ann_file=data_root + 'coco_voc2012_cat/annotations/instances_train.json'),
    val=dict(
        img_prefix=data_root + 'VOC2012/JPEGImages/',
        classes=classes,
        ann_file=data_root + 'coco_voc2012_cat/annotations/instances_val.json'),
    test=dict(
        img_prefix=data_root + 'VOC2012/JPEGImages/',
        classes=classes,
        ann_file=data_root + 'coco_voc2012_cat/annotations/instances_val.json'))
evaluation = dict(interval=100)

# Modify schedule related settings
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
total_epochs = 10000

# Modify runtime related settings
checkpoint_config = dict(interval=10)

# We can use the pre-trained model to obtain higher performance
# load_from = 'checkpoints/*.pth'
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• modelconfigurationnum_classes=1Is the number of categories
• datasetConfigure a custom data set for preparation
• scheduleConfigured trainedlrAnd iteration roundstotal_epochs
• runtimeconfigurablecheckpointHow many intervals to save one. The default is 1 epoch, and the space is insufficient 😶

The configuration can be overridden with __base__ content, as described in the official documentation.

Training model

# single-gpu training
python tools/train.py \
configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \
--work-dir _train_voc_cat

# multi-gpu training
bash ./tools/dist_train.sh \
configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \
2 \
--work-dir _train_voc_cat
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When the breakpoint is restored,

bash ./tools/dist_train.sh \
configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \
2 \
--work-dir _train_voc_cat \
--resume-from _train_voc_cat/epoch_100.pth
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ModuleNotFoundError: No module named ‘PycocoTools’

pip uninstall pycocotools mmpycocotools
pip install mmpycocotools
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Viewing training Loss

pip install seaborn

python tools/analyze_logs.py plot_curve \
_train_voc_cat/*.log.json \
--keys loss_cls loss_bbox \
--legend loss_cls loss_bbox
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See log.json for keys.

The test model

# single-gpu testingpython tools/test.py \ configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \ _train_voc_cat/latest.pth \ --out results.pkl \ -eval bbox \
--show

# multi-gpu testing
bash tools/dist_test.sh \
configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \
_train_voc_cat/latest.pth \
2 \
--out results.pkl \
--eval bbox
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