Link For Notebbok: https://colab.research.google.com/drive/1EIt6GyWeNy2qPT0170c-jFxPFiHcovrZ?usp=sharing
Link for dataset: https://drive.google.com/file/d/1Vrn_K7yWRrvQqKNMGVm71_oc7VbqlISr/view?usp=sharing
Instance segmentation involves not only detecting objects in an image, but also segmenting them into individual instances.
Train a YOLO model on a dataset that includes annotations for both object detection and segmentation. The annotations should include bounding boxes for the objects as well as segmentation masks for each instance.
Modify the YOLO architecture to output both the bounding boxes and segmentation masks for each detected object. This can be done by adding additional output channels to the network.
During inference, run the YOLO model on an input image and use the output segmentation masks to segment each object into its individual instance. This can be done by applying a segmentation algorithm, such as watershed or mean shift, to the segmentation masks.
Finally, post-process the segmented instances to refine the results and remove any false positives or duplicates.
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
1/10 4.66G 2.262 5.927 5.814 2.43 6 640: 100% 21/21 [00:17<00:00, 1.23it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.60it/s]
all 73 102 0.173 0.28 0.152 0.0734 0.128 0.179 0.115 0.0341
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
2/10 5.94G 1.756 3.729 2.817 2.06 5 640: 100% 21/21 [00:08<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:02<00:00, 1.34it/s]
all 73 102 0.362 0.44 0.311 0.132 0.321 0.332 0.243 0.0768
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
3/10 5.94G 1.533 3.05 2.135 1.876 5 640: 100% 21/21 [00:08<00:00, 2.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:02<00:00, 1.32it/s]
all 73 102 0.522 0.45 0.395 0.183 0.413 0.341 0.299 0.109
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
4/10 5.94G 1.427 2.735 1.859 1.77 8 640: 100% 21/21 [00:08<00:00, 2.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.53it/s]
all 73 102 0.552 0.357 0.383 0.198 0.581 0.365 0.383 0.158
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
5/10 5.94G 1.497 2.561 1.836 1.845 3 640: 100% 21/21 [00:08<00:00, 2.48it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.69it/s]
all 73 102 0.67 0.536 0.584 0.322 0.659 0.524 0.525 0.193
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
6/10 5.94G 1.482 2.462 1.632 1.788 8 640: 100% 21/21 [00:09<00:00, 2.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.79it/s]
all 73 102 0.504 0.478 0.447 0.216 0.411 0.393 0.358 0.152
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
7/10 5.94G 1.475 2.51 1.51 1.763 3 640: 100% 21/21 [00:09<00:00, 2.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.75it/s]
all 73 102 0.551 0.493 0.472 0.228 0.485 0.414 0.401 0.172
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
8/10 5.94G 1.349 2.264 1.34 1.656 3 640: 100% 21/21 [00:09<00:00, 2.11it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.79it/s]
all 73 102 0.497 0.473 0.418 0.206 0.476 0.464 0.354 0.153
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
9/10 5.94G 1.245 2.111 1.253 1.612 3 640: 100% 21/21 [00:09<00:00, 2.12it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:01<00:00, 1.74it/s]
all 73 102 0.543 0.611 0.583 0.315 0.56 0.639 0.578 0.258
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
10/10 5.94G 1.216 2.165 1.291 1.591 6 640: 100% 21/21 [00:09<00:00, 2.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:03<00:00, 1.31s/it]
all 73 102 0.649 0.629 0.646 0.356 0.707 0.665 0.69 0.302
YOLOv8s-seg summary (fused): 195 layers, 11780374 parameters, 0 gradients, 42.4 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 3/3 [00:03<00:00, 1.21s/it]
all 73 102 0.665 0.627 0.647 0.357 0.715 0.665 0.691 0.302
Cracks-and-spalling 73 44 0.698 0.788 0.777 0.442 0.768 0.864 0.866 0.44
object 73 58 0.633 0.466 0.517 0.273 0.662 0.466 0.516 0.165
For Training 1330 images are used For Testing 33 images are used For Validation 33 images are used
The YOLO model is based on a deep convolutional neural network (CNN) that takes an input image and outputs a set of bounding boxes, each representing a detected object along with a confidence score that reflects how confident the model is in the accuracy of the detection. The YOLO algorithm processes the entire image at once, unlike many other object detection models that use a sliding window approach. This approach makes YOLO very fast and efficient for real-time object detection applications.
YOLO V7 consists of a backbone CNN, followed by a neck and head. The backbone CNN is a series of convolutional layers that extract features from the input image. The neck is a set of additional convolutional layers that help the model to integrate contextual information from different scales. Finally, the head is a set of convolutional layers that predict the bounding boxes and class probabilities.
https://colab.research.google.com/drive/1IMdgwh9X3emRhwiMPITAa342FEjKeqMX?usp=sharing
hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 Model Summary: 415 layers, 37234314 parameters used
all 33 90 0.724 0.333 0.321 0.144
Crack 33 17 0.849 0.412 0.46 0.171
Efflorescence 33 38 0.371 0.395 0.281 0.11
Rebar 33 21 0.523 0.476 0.465 0.175
Rust 33 5 1 0 0 0
Scaling 33 2 1 0 0 0
Spall 33 7 0.599 0.714 0.718 0.407
5 epochs completed in 1.392 hours.
