mmdetection入坑日記（三）開始訓練

-----訓練階段

數據集準備

上一篇博客已經註明了數據集的準備

設置 lr

lr = 0.00125 * num_gpus * samples_per_gpu

設置 coco_detection.py

修改 root 目錄，以及 train，val，json 的目錄

mm2.0中的config設置

_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
classes = ('person', 'bicycle', 'car')
data = dict(
    train=dict(classes=classes),
    val=dict(classes=classes),
    test=dict(classes=classes))
# TODO: Update model url after bumping to V2.0
load_from = 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'  # noqa

Train with a single GPU

python tools/train.py ${CONFIG_FILE} [optional arguments]

If you want to specify the working directory in the command, you can add an argument --work_dir ${YOUR_WORK_DIR}.

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

Optional arguments are:
--no-validate (not suggested): By default, the codebase will perform evaluation at every k (default value is 1, which can be modified like this) epochs during the training. To disable this behavior, use --no-validate.
--work-dir ${WORK_DIR}: Override the working directory specified in the config file.
--resume-from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.
Difference between resume-from and load-from: resume-from loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. load-from only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.

-----測試階段

Image demo

We provide a demo script to test a single image.

python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}]

Examples:

python demo/image_demo.py demo/demo.jpg configs/faster_rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth --device cpu

Webcam demo

We provide a webcam demo to illustrate the results.

python demo/webcam_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--camera-id ${CAMERA-ID}] [--score-thr ${SCORE_THR}]

Examples:

python demo/webcam_demo.py configs/faster_rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth

PS：如果想要自己去測試數據集的結果，可以自己根據這兩個demo改一下。