Loads checkpoint by local backend from path: ./model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth
12/05 15:22:56 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "function" registry tree. As a workaround, the current "function" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized.
/home/mixaill76/.local/lib/python3.10/site-packages/mmengine/visualization/visualizer.py:196: UserWarning: Failed to add <class 'mmengine.visualization.vis_backend.LocalVisBackend'>, please provide the `save_dir` argument.
warnings.warn(f'Failed to add {vis_backend.__class__}, '
loading annotations into memory...
Done (t=0.32s)
creating index...
index created!
loading annotations into memory...
Done (t=0.38s)
creating index...
index created!
0%| | 0/5000 [00:00<?, ?it/s]/home/mixaill76/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)
return F.conv2d(input, weight, bias, self.stride,
/home/mixaill76/.local/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3526.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
100%|███████████████████████████████████████| 5000/5000 [03:39<00:00, 22.74it/s]
Evaluating bbox...
Loading and preparing results...
DONE (t=0.80s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=15.93s).
Accumulating evaluation results...
DONE (t=3.68s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.405
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.576
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.440
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.446
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.578
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.540
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.338
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.638
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.765
Evaluating segm...
Loading and preparing results...
DONE (t=1.57s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=18.35s).
Accumulating evaluation results...
DONE (t=3.67s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.376
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.566
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.302
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.473
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
bbox Results (%)
┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ bbox_mAP ┃ bbox_mAP_50 ┃ bbox_mAP_75 ┃ bbox_mAP_s ┃ bbox_mAP_m ┃ bbox_mAP_l ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 40.49 │ 57.60 │ 43.96 │ 20.66 │ 44.64 │ 57.83 │
└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘
segm Results (%)
┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ segm_mAP ┃ segm_mAP_50 ┃ segm_mAP_75 ┃ segm_mAP_s ┃ segm_mAP_m ┃ segm_mAP_l ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 35.39 │ 55.10 │ 37.65 │ 13.06 │ 38.34 │ 56.64 │
└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘
coco_metric.compute() : 55.512
Evaluating bbox...
Evaluating segm...
bbox Results (%)
┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ bbox_mAP ┃ bbox_mAP_50 ┃ bbox_mAP_75 ┃ bbox_mAP_s ┃ bbox_mAP_m ┃ bbox_mAP_l ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 40.49 │ 57.60 │ 43.96 │ 20.66 │ 44.64 │ 57.83 │
└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘
segm Results (%)
┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ segm_mAP ┃ segm_mAP_50 ┃ segm_mAP_75 ┃ segm_mAP_s ┃ segm_mAP_m ┃ segm_mAP_l ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 35.39 │ 55.10 │ 37.65 │ 13.06 │ 38.34 │ 56.64 │
└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘
faster_coco_metric.compute() : 27.748
0.4998470015873722