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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

chlorine, nitrous oxide (laughing gas), metallic fumes from construction and industrial air particles, like complex dust

It tucks its legs in, close to its body, and just barely clears the barbed spikes at the top of the fence

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