How to save the model weights after running train_detector in mmdetection?

Question

cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 600
# Change the evaluation metric since we use customized dataset.
cfg.evaluation.metric = 'bbox'
# We can set the evaluation interval to reduce the evaluation times
cfg.evaluation.interval = 3
# We can set the checkpoint saving interval to reduce the storage cost
cfg.checkpoint_config.interval = 3
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)

cfg.load_from = 'gdrive/My Drive/mmdetection/checkpoints/vfnet_r50_fpn_mdconv_c3- 
c5_mstrain_2x_coco_20201027pth-6879c318.pth'
cfg.work_dir = "../vinbig"
cfg.runner.max_epochs = 6
cfg.total_epochs = 6model = build_detector(cfg.model)
datasets = [build_dataset(cfg.data.train)]
train_detector(model, datasets[0], cfg, distributed=False, validate=True)

Now,my question is once I have finetuned the model on my custom dataset,how do i use it for testing? Where is the finetuned model stored? At most of the places, the model is immediately used for testing but how do I save the finetuned model to be tested later on.

img = mmcv.imread('kitti_tiny/training/image_2/000068.jpeg')

model.cfg = cfg
result = inference_detector(model, img)
show_result_pyplot(model, img, result)

The above is what occurs mostly after the training phase. But that is because the model is already in runtime.How can i create my own mmdetection model checkpoint? I have been working on Google colab.

Answer 1

Well, I don't know how to do it manually, but your checkpoints are automatically saved in cfg.work_dir = "../vinbig". There you can find 'latest.pth' file as your final checkpoint.