在Google Colab笔记本中，detectron2在训练过程中没有进行评估，如这里所示。在训练自定义数据集时，它不使用验证数据。我应该如何添加这个功能？
参考Github仓库 – https://github.com/facebookresearch/detectron2
回答：
Colab笔记本通常运行速度较慢，旨在展示一个仓库的基本使用方式。训练过程中不进行评估可能仅仅是因为他们认为在简单的笔记本中不需要这一步。仓库中包含了在训练过程中定期进行评估的更复杂示例。
然而，如果你仍然想在笔记本中进行评估，我看到他们在这里创建了训练和验证集的划分：
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"])但由于以下这行代码，在训练过程中没有进行评估：
cfg.DATASETS.TEST = ()尝试
cfg.DATASETS.TEST = ("balloon_val",)然后设置训练器的钩子，以便满足你的评估需求
通过将eval_period设置为50，并在balloon_val上将自定义评估设置为COCOEvaluator，获得的结果如下：
[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 |