pythonfacebookcomputer-visionartificial-intelligencedetectron

How to make detectron2 train on validation dataset in colab notebook?


There is no evaluation during training in the Google Colab Notebook for detectron2 as can be seen here: https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5. It does not use the validation data while training a custom dataset. How should I add it?

Reference Github repo - https://github.com/facebookresearch/detectron2


Solution

  • Colab notebooks are usually slow and meant for showing the basic usage of a repo. The fact that it is not evaluating during training could be just as simple as they don't consider it necessary having that step in a simple notebook. The more complex examples with periodic evaluation during training are in their repo.

    However if you still want to evaluate in the notebook, I see that they create a train and val split here:

    for d in ["train", "val"]:
        DatasetCatalog.register("balloon_" + d, lambda d=d: get_balloon_dicts("balloon/" + d))
        MetadataCatalog.get("balloon_" + d).set(thing_classes=["balloon"])
    

    But is is not evaluating under training because of this line of code

    cfg.DATASETS.TEST = ()
    

    Try

    cfg.DATASETS.TEST = ("balloon_val",)
    

    instead and then set the hook for the trainer such that it suits your evaluation needs

    Results obtained by setting eval_period to 50 and the custom evaluation to COCOEvaluator on balloon_val:

    
    [07/14 07:23:52 d2.engine.train_loop]: Starting training from iteration 0
    [07/14 07:24:02 d2.utils.events]:  eta: 0:02:14  iter: 19  total_loss: 2.246  loss_cls: 0.7813  loss_box_reg: 0.6616  loss_mask: 0.683  loss_rpn_cls: 0.03956  loss_rpn_loc: 0.008304  time: 0.4848  data_time: 0.0323  lr: 1.6068e-05  max_mem: 5425M
    [07/14 07:24:12 d2.utils.events]:  eta: 0:02:01  iter: 39  total_loss: 1.879  loss_cls: 0.6221  loss_box_reg: 0.5713  loss_mask: 0.615  loss_rpn_cls: 0.04036  loss_rpn_loc: 0.01448  time: 0.4721  data_time: 0.0108  lr: 3.2718e-05  max_mem: 5425M
    [07/14 07:24:17 d2.evaluation.evaluator]: Start inference on 13 batches
    [07/14 07:24:26 d2.evaluation.evaluator]: Inference done 11/13. Dataloading: 0.0013 s/iter. Inference: 0.1507 s/iter. Eval: 0.1892 s/iter. Total: 0.3412 s/iter. ETA=0:00:00
    [07/14 07:24:27 d2.evaluation.evaluator]: Total inference time: 0:00:02.799655 (0.349957 s / iter per device, on 1 devices)
    [07/14 07:24:27 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:01 (0.149321 s / iter per device, on 1 devices)
    [07/14 07:24:27 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
    [07/14 07:24:27 d2.evaluation.coco_evaluation]: Saving results to ./output/coco_instances_results.json
    [07/14 07:24:27 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.029
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.063
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.021
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.044
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.035
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.176
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.444
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.388
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510
    [07/14 07:24:27 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
    |  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
    |:-----:|:------:|:------:|:-----:|:-----:|:-----:|
    | 2.906 | 6.326  | 2.098  | 0.193 | 4.398 | 3.484 |
    Loading and preparing results...
    DONE (t=0.02s)
    creating index...
    index created!
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.02 seconds.
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:24:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.040
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.081
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.039
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.049
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.062
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.004
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.214
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.532
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.465
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.613
    [07/14 07:24:27 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
    |  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
    |:-----:|:------:|:------:|:-----:|:-----:|:-----:|
    | 4.027 | 8.132  | 3.905  | 0.166 | 4.904 | 6.221 |
    [07/14 07:24:32 d2.utils.events]:  eta: 0:01:56  iter: 59  total_loss: 1.621  loss_cls: 0.4834  loss_box_reg: 0.6684  loss_mask: 0.4703  loss_rpn_cls: 0.03119  loss_rpn_loc: 0.006103  time: 0.4799  data_time: 0.0117  lr: 4.9367e-05  max_mem: 5425M
    [07/14 07:24:42 d2.utils.events]:  eta: 0:01:47  iter: 79  total_loss: 1.401  loss_cls: 0.3847  loss_box_reg: 0.6159  loss_mask: 0.3641  loss_rpn_cls: 0.03303  loss_rpn_loc: 0.00822  time: 0.4797  data_time: 0.0130  lr: 6.6017e-05  max_mem: 5425M
    [07/14 07:24:51 d2.utils.events]:  eta: 0:01:36  iter: 99  total_loss: 1.268  loss_cls: 0.3295  loss_box_reg: 0.6366  loss_mask: 0.2884  loss_rpn_cls: 0.01753  loss_rpn_loc: 0.00765  time: 0.4775  data_time: 0.0096  lr: 8.2668e-05  max_mem: 5425M
    [07/14 07:24:51 d2.evaluation.evaluator]: Start inference on 13 batches
    [07/14 07:25:01 d2.evaluation.evaluator]: Inference done 11/13. Dataloading: 0.0014 s/iter. Inference: 0.1493 s/iter. Eval: 0.1851 s/iter. Total: 0.3358 s/iter. ETA=0:00:00
    [07/14 07:25:01 d2.evaluation.evaluator]: Total inference time: 0:00:02.778349 (0.347294 s / iter per device, on 1 devices)
    [07/14 07:25:01 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:01 (0.148906 s / iter per device, on 1 devices)
    [07/14 07:25:02 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
    [07/14 07:25:02 d2.evaluation.coco_evaluation]: Saving results to ./output/coco_instances_results.json
    [07/14 07:25:02 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.543
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.751
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.626
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.092
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.636
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.196
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.620
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.714
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.533
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.588
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803
    [07/14 07:25:02 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
    |   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |
    |:------:|:------:|:------:|:-----:|:------:|:------:|
    | 54.340 | 75.066 | 62.622 | 9.181 | 47.208 | 63.594 |
    Loading and preparing results...
    DONE (t=0.02s)
    creating index...
    index created!
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.02 seconds.
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:25:02 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.630
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.754
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.741
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.060
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.750
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.214
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.692
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.786
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.533
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.641
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.893
    [07/14 07:25:02 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
    |   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |
    |:------:|:------:|:------:|:-----:|:------:|:------:|
    | 62.959 | 75.390 | 74.088 | 5.987 | 51.899 | 74.988 |
    [07/14 07:25:11 d2.utils.events]:  eta: 0:01:26  iter: 119  total_loss: 1.158  loss_cls: 0.2745  loss_box_reg: 0.6951  loss_mask: 0.2165  loss_rpn_cls: 0.02461  loss_rpn_loc: 0.00421  time: 0.4773  data_time: 0.0101  lr: 9.9318e-05  max_mem: 5425M
    [07/14 07:25:21 d2.utils.events]:  eta: 0:01:16  iter: 139  total_loss: 1.015  loss_cls: 0.1891  loss_box_reg: 0.6029  loss_mask: 0.1745  loss_rpn_cls: 0.02219  loss_rpn_loc: 0.005621  time: 0.4766  data_time: 0.0111  lr: 0.00011597  max_mem: 5425M
    [07/14 07:25:26 d2.evaluation.evaluator]: Start inference on 13 batches
    [07/14 07:25:34 d2.evaluation.evaluator]: Inference done 11/13. Dataloading: 0.0013 s/iter. Inference: 0.1459 s/iter. Eval: 0.1786 s/iter. Total: 0.3258 s/iter. ETA=0:00:00
    [07/14 07:25:35 d2.evaluation.evaluator]: Total inference time: 0:00:02.608437 (0.326055 s / iter per device, on 1 devices)
    [07/14 07:25:35 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:01 (0.143658 s / iter per device, on 1 devices)
    [07/14 07:25:35 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
    [07/14 07:25:35 d2.evaluation.coco_evaluation]: Saving results to ./output/coco_instances_results.json
    [07/14 07:25:35 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.663
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.843
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.754
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.245
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.790
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.228
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.712
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.758
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.567
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.647
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.840
    [07/14 07:25:35 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 66.307 | 84.257 | 75.431 | 24.466 | 56.175 | 79.035 |
    Loading and preparing results...
    DONE (t=0.01s)
    creating index...
    index created!
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.02 seconds.
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:25:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.756
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.839
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.833
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.581
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.915
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.248
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.788
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.836
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.600
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.676
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.950
    [07/14 07:25:35 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 75.579 | 83.916 | 83.342 | 13.466 | 58.113 | 91.479 |
    [07/14 07:25:40 d2.utils.events]:  eta: 0:01:07  iter: 159  total_loss: 0.845  loss_cls: 0.1613  loss_box_reg: 0.5442  loss_mask: 0.1211  loss_rpn_cls: 0.01358  loss_rpn_loc: 0.006381  time: 0.4768  data_time: 0.0110  lr: 0.00013262  max_mem: 5425M
    [07/14 07:25:49 d2.utils.events]:  eta: 0:00:58  iter: 179  total_loss: 0.7381  loss_cls: 0.1207  loss_box_reg: 0.4569  loss_mask: 0.1153  loss_rpn_cls: 0.01103  loss_rpn_loc: 0.005893  time: 0.4782  data_time: 0.0098  lr: 0.00014927  max_mem: 5425M
    [07/14 07:25:59 d2.utils.events]:  eta: 0:00:48  iter: 199  total_loss: 0.5811  loss_cls: 0.108  loss_box_reg: 0.3294  loss_mask: 0.09868  loss_rpn_cls: 0.01414  loss_rpn_loc: 0.008676  time: 0.4783  data_time: 0.0101  lr: 0.00016592  max_mem: 5425M
    [07/14 07:25:59 d2.evaluation.evaluator]: Start inference on 13 batches
    [07/14 07:26:05 d2.evaluation.evaluator]: Inference done 11/13. Dataloading: 0.0017 s/iter. Inference: 0.1317 s/iter. Eval: 0.0985 s/iter. Total: 0.2319 s/iter. ETA=0:00:00
    [07/14 07:26:05 d2.evaluation.evaluator]: Total inference time: 0:00:01.788219 (0.223527 s / iter per device, on 1 devices)
    [07/14 07:26:05 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:01 (0.127455 s / iter per device, on 1 devices)
    [07/14 07:26:05 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
    [07/14 07:26:05 d2.evaluation.coco_evaluation]: Saving results to ./output/coco_instances_results.json
    [07/14 07:26:05 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.728
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.894
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.858
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.303
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.571
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.848
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.218
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.742
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.790
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.688
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.877
    [07/14 07:26:05 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 72.797 | 89.384 | 85.752 | 30.301 | 57.057 | 84.812 |
    Loading and preparing results...
    DONE (t=0.01s)
    creating index...
    index created!
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:26:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.805
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.885
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.880
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.617
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.950
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.250
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.808
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.860
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.567
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.960
    [07/14 07:26:05 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 80.490 | 88.547 | 87.953 | 25.206 | 61.723 | 94.959 |
    [07/14 07:26:15 d2.utils.events]:  eta: 0:00:38  iter: 219  total_loss: 0.4771  loss_cls: 0.08176  loss_box_reg: 0.2226  loss_mask: 0.09229  loss_rpn_cls: 0.01647  loss_rpn_loc: 0.009867  time: 0.4789  data_time: 0.0132  lr: 0.00018257  max_mem: 5425M
    [07/14 07:26:25 d2.utils.events]:  eta: 0:00:28  iter: 239  total_loss: 0.366  loss_cls: 0.07189  loss_box_reg: 0.1961  loss_mask: 0.08049  loss_rpn_cls: 0.01413  loss_rpn_loc: 0.006811  time: 0.4785  data_time: 0.0122  lr: 0.00019922  max_mem: 5425M
    [07/14 07:26:29 d2.evaluation.evaluator]: Start inference on 13 batches
    [07/14 07:26:34 d2.evaluation.evaluator]: Inference done 11/13. Dataloading: 0.0015 s/iter. Inference: 0.1195 s/iter. Eval: 0.0502 s/iter. Total: 0.1711 s/iter. ETA=0:00:00
    [07/14 07:26:34 d2.evaluation.evaluator]: Total inference time: 0:00:01.375643 (0.171955 s / iter per device, on 1 devices)
    [07/14 07:26:34 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:00 (0.117491 s / iter per device, on 1 devices)
    [07/14 07:26:34 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
    [07/14 07:26:34 d2.evaluation.coco_evaluation]: Saving results to ./output/coco_instances_results.json
    [07/14 07:26:34 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.779
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.916
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.878
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.350
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.615
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.234
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.800
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.826
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.467
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.718
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.923
    [07/14 07:26:34 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 77.888 | 91.606 | 87.774 | 34.965 | 61.497 | 89.576 |
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:26:34 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.823
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.894
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.891
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.248
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.624
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.967
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.254
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.832
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.858
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.741
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.973
    [07/14 07:26:34 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 82.323 | 89.379 | 89.068 | 24.752 | 62.427 | 96.691 |
    [07/14 07:26:39 d2.utils.events]:  eta: 0:00:19  iter: 259  total_loss: 0.2651  loss_cls: 0.05436  loss_box_reg: 0.1442  loss_mask: 0.06249  loss_rpn_cls: 0.005261  loss_rpn_loc: 0.00489  time: 0.4781  data_time: 0.0123  lr: 0.00021587  max_mem: 5425M
    [07/14 07:26:49 d2.utils.events]:  eta: 0:00:09  iter: 279  total_loss: 0.4224  loss_cls: 0.07591  loss_box_reg: 0.1941  loss_mask: 0.09489  loss_rpn_cls: 0.009817  loss_rpn_loc: 0.008633  time: 0.4777  data_time: 0.0109  lr: 0.00023252  max_mem: 5425M
    [07/14 07:26:59 d2.utils.events]:  eta: 0:00:00  iter: 299  total_loss: 0.3534  loss_cls: 0.07829  loss_box_reg: 0.1646  loss_mask: 0.08058  loss_rpn_cls: 0.01157  loss_rpn_loc: 0.006635  time: 0.4779  data_time: 0.0120  lr: 0.00024917  max_mem: 5425M
    [07/14 07:27:00 d2.engine.hooks]: Overall training speed: 298 iterations in 0:02:22 (0.4779 s / it)
    [07/14 07:27:00 d2.engine.hooks]: Total training time: 0:03:06 (0:00:43 on hooks)
    [07/14 07:27:00 d2.evaluation.evaluator]: Start inference on 13 batches
    [07/14 07:27:04 d2.evaluation.evaluator]: Inference done 11/13. Dataloading: 0.0015 s/iter. Inference: 0.1155 s/iter. Eval: 0.0340 s/iter. Total: 0.1510 s/iter. ETA=0:00:00
    [07/14 07:27:04 d2.evaluation.evaluator]: Total inference time: 0:00:01.238510 (0.154814 s / iter per device, on 1 devices)
    [07/14 07:27:04 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:00 (0.114618 s / iter per device, on 1 devices)
    [07/14 07:27:04 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
    [07/14 07:27:04 d2.evaluation.coco_evaluation]: Saving results to ./output/coco_instances_results.json
    [07/14 07:27:04 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.762
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.927
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.859
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.310
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.640
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.864
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.236
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.788
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.814
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.433
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.724
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.903
    [07/14 07:27:04 d2.evaluation.coco_evaluation]: Evaluation results for bbox: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 76.245 | 92.732 | 85.874 | 31.015 | 63.981 | 86.418 |
    Loading and preparing results...
    DONE (t=0.00s)
    creating index...
    index created!
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
    [07/14 07:27:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.818
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.902
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.899
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.253
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.632
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.956
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.252
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.828
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.856
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.970
    [07/14 07:27:04 d2.evaluation.coco_evaluation]: Evaluation results for segm: 
    |   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
    |:------:|:------:|:------:|:------:|:------:|:------:|
    | 81.780 | 90.213 | 89.900 | 25.284 | 63.179 | 95.585 |