deep-learning-for-image-processing
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Published
20 hours ago •
WZMIAOMIAO
WZMIAOMIAO
Added MultiScaleRoIPooling Module
Added MultiScaleRoIPooling Module and run this config renset50+FPN+Faster_RCNN+RoIPooling.
Keeping other parameters fixed, using batch_size=8 in 4 NVIDIA GeForce RTX 3080 machine, num_workers=8, momentum=0.9, weight_decay=1e-4, epochs=100, lr-steps=[25, 50], Finetuning on the PASCAL_VOC_2012.
result at epoch:58 as follows
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.657
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.178
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.359
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.347
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.528
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.537
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.296
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.552
