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Training Details (Skip this part if you arent interested in AI programming) - I used 2000 synthetic 512x512 px training images (plus 200 synthetic validation images) - I did ZERO training on real images - I trained the "heads" for 4 epochs of 500 steps and the full network for another 4 epochs - Resnet50 backbone (Resnet101 caused OOM errors) using COCO pre-trained weights - Training took about 20 minutes on my computer - Final losses after the 8th epoch: loss: 0.3615 - rpn_class_loss: 0.0023 - rpn_bbox_loss: 0.1823 - mrcnn_class_loss: 0.0232 - mrcnn_bbox_loss: 0.0636 - mrcnn_mask_loss: 0.0901 - val_loss: 0.3261 - val_rpn_class_loss: 0.0013 - val_rpn_bbox_loss: 0.1875 - val_mrcnn_class_loss: 0.0043 - val_mrcnn_bbox_loss: 0.0535 - val_mrcnn_mask_loss: 0.0794 Results It's far from perfect, and probably isn't practical to bolt onto a robot right now, but with more training and improvements in GPU hardware, I think this could be a viable solution to pick up cigarette butts on a huge scale

Archived from the original on 22 March 2012

Among them are earth, leather, anise, and baking spice

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