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anonymous-submission-dataset-code/TiBuDB_trained_weights

sourceHugging Faceetalab-2.0updated 5mo agoView on Hugging Face

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Model Weights

The trained weights for all benchmarks are hosted on Hugging Face.

Weights Organization

Download the weights and place them in the TiBuDB_trained_weights/ directory.

Task	Model	Weight File	Description	SAHI Crop Size	Inference Size
Detection	YOLO26x	`best_det_yolo26x_seed1000_baseline.pt`	Baseline (1x)	128	128
Detection	YOLO26x	`best_det_yolo26x_seed1000_x4.pt`	Upscaled (4x)	128	512
Detection	RF-DETR	`best_ema_det_rfdetr_large_seed0_baseline.pth`	Transformer Baseline	128	N/A
Segmentation	YOLO26x	`best_seg_yolo26x_seed100_baseline.pt`	Baseline (1x)	128	128
Segmentation	YOLO26x	`best_seg_yolo26x_seed100_x4.pt`	Upscaled (4x)	128	512
Segmentation	RF-DETR	`best_ema_seg_rfdetr_large_seed100_baseline.pth`	Transformer Baseline	128	N/A
OBB	YOLO26x	`best_obb_yolo26x_seed5000_baseline.pt`	Oriented Bbox (1x)	128	128
OBB	YOLO26x	`best_obb_yolo26x_seed5000_x4.pt`	Oriented Bbox (4x)	128	512

Note: RF-DETR processes images at the native crop size (128) without upscaling; inference size is not applicable.

Quick Load Example

Ultralytics (YOLO / RT-DETR)

python

from ultralytics import YOLO

model = YOLO("TiBuDB_trained_weights/best_det_yolo26x_seed1000_baseline.pt")
results = model.predict("path/to/image.png")

RF-DETR

python

from rfdetr import RFDETRLarge

model = RFDETRLarge(pretrain_weights="TiBuDB_trained_weights/best_ema_det_rfdetr_large_seed0_baseline.pth")
results = model.predict("path/to/image.png")