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detr_balloon_example

Object Detection
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detr
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Original result 

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.018
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.028
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.018
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.040
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.136
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.468
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.393
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.557
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

After training result 

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.581
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.740
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.661
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.580
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.216
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.686
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.704
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.615
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.809
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

Config

  • dataset: NIH
  • original model: facebook/detr-resnet-50
  • lr: 5e-06
  • dropout_rate: 0.1
  • weight_decay: 0.05
  • max_epochs: 30
  • train samples: 61

Logging

Training process 

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Examples

{'size': tensor([ 800, 1066]), 'image_id': tensor([0]), 'class_labels': tensor([0]), 'boxes': tensor([[0.5955, 0.5811, 0.2202, 0.3561]]), 'area': tensor([3681.5083]), 'iscrowd': tensor([0]), 'orig_size': tensor([1536, 2048])}

Example

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