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)

现在，我的问题是，一旦我对自定义数据集的模型进行了微调，我如何使用它进行测试？微调后的模型存储在哪里？在大多数地方，模型会立即用于测试，但我如何保存微调后的模型以供稍后测试。

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

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

以上是在训练阶段之后发生的事情。但那是因为模型已经在运行时了。我如何创建我自己的mmdetection模型检查点呢？我一直在Google colab上工作。