Feature/yolov26 - #153
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… TensorPredictions.plot
…king change in 8.3.125
…avoid memory-leak and OOM on large images
…at-bug into develop
- Bump ultralytics upper bound to 8.4.49 (required for YOLOv26) - Update postprocess() in yolo_helpers.py to handle the end2end output format introduced by YOLOv26 (post-NMS xyxy detections vs legacy pre-NMS xywh anchors); also handle the 8.4.x tuple output wrapping for legacy YOLO11 models - Fix trainers.py to use load_checkpoint (ultralytics >= 8.4 renamed attempt_load_one_weight) and yaml_load (replaced by YAML.load) - Add scripts/training/fb_config_yolo26n.yaml for training with yolo26n-seg Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace the internal-API hack (pred.setup_model(self=pred, ...)) with direct access to yolo.model. The setup_model call in ultralytics 8.4 now requires args.end2end which the dict2attr stub did not provide. For .pt models the AutoBackend wrapper is unnecessary since flat-bug preprocesses images itself and only uses PyTorch tensors. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- fb_train.py: change default model from yolov8m-seg.pt to yolo26m-seg.pt - fb_config_M40S.yaml: update to yolo26m-seg.pt - fb_config_M40S_GHPC.yaml: fix yolov26m.pt (wrong name/variant) to yolo26m-seg.pt Pretrained weights are auto-downloaded by ultralytics if not present locally. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ultralytics < 8.4 does not call attempt_download_asset inside torch_safe_load, so yolo26m-seg.pt (and any other asset-list model) was never downloaded on machines running 8.3.x. Add an explicit attempt_download_asset call in setup_model before _load_checkpoint. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
YOLOv26's one2many loss uses significantly more memory during validation than YOLOv8 did. Call torch.cuda.empty_cache() before each validation pass to release fragmented reserved memory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ultralytics doubles batch_size for validation (non-OBB tasks), but YOLOv26's one2many loss makes this OOM on memory-constrained GPUs. Clamp validation batch_size back to self.args.batch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Default compression level 6 is slow for large batches of crops. Level 1 is significantly faster at the cost of slightly larger files. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
YOLOv26's one2many head assigns many positive anchors per GT instance. On dense flat-bug images (hundreds of insects per 1024×1024 crop) this causes single_mask_loss/crop_mask to materialise a [n_pos, 256, 256] float32 tensor of ~11.5 GB — OOM even at batch_size=1. The validation loss is only logged and has no effect on fitness, mAP, or early stopping. We therefore replace model.loss (on both the live model and the EMA model) with a no-op returning zeros for the duration of each validation pass, then restore the class method via del. This is orthogonal to the earlier batch_size and empty_cache mitigations, which did not address the per-image root cause. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
YOLOv26's validator initialises self.loss with 5 components; our no-op was returning torch.zeros(4) from len(self.loss_names). Return scalar 0 instead — adding a scalar to any tensor is always valid. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Add support for YOLOv26 models by updating flatbug compatibility to
ultralytics>=8.4.0,<=8.4.90and changed the default model toflat_bug_M_v2.ptwhich is such a newly trained YOLOv26 Medium flatbug model with the following evaluation statistics:Which is marginally lower Precision (92.3 vs 92.4) but better Recall (96.4 vs 95.0) and F1 (93.7 vs 93.1) as compared to the original
flat_bug_M.ptmodel.Also contains some linting and package management improvements.