source activate open-mmlab

├── mmdetection
    |── data
        ├── CocoCust
            └── annotations
            └── coco
            └── fakeKitti

python tools/train.py configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco_cust.py --gpus 1 --work-dir ./fcos_r50_fpn_gn_coco_cust

├── mmdetection
    |── data
        ├── CocoCust
            └── annotations

python tools/train.py configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco_cust_loadFromSeparatedFile.py --gpus 1 --work-dir ./fcos_r50_fpn_gn_coco_cust

Here, the example-data already exists in this repo.

Note 1.: As an example, train2017 is same to val2017 which will indicate how the model overfit our training-data.

        2.: Here, 'workers_per_gpu' was set to 0. If more workers were set, if will occurs an out of shared memory ERROR due to a docker environment configuretion.

tensorboard --logdir=./fcos_r50_fpn_gn_coco_cust/tf_logs/ --bind_all

http://127.0.0.1:6006/

docker attach mmdetection

Note: As an example, train2017 is same to val2017 which will indicate how the model overfit our training-data.

Please refer to get_started.md for installation.

Please see get_started.md for the basic usage of MMDetection. We provide colab tutorial, and full guidance for quick run with existing dataset and with new dataset for beginners. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, customizing models, customizing runtime settings and useful tools.

Please refer to FAQ for frequently asked questions.

MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.