Downloads · 30 days
0
anonymous-submission-dataset-code
anonymous-submission-dataset-code/TiBuDB_trained_weights
TiBuDB_trained_weights is a machine learning model from anonymous-submission-dataset-code. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as etalab-2.0.
The trained weights for all benchmarks are hosted on Hugging Face.
Downloads · 30 days
0
Access
Public
Updated May 7, 2026
Repo size
1 GB
Likes
0
Public
Click a slice to open those files.
.pt772 MB · 74%
From the Hugging Face model README
The trained weights for all benchmarks are hosted on Hugging Face.
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.
Ultralytics (YOLO / RT-DETR)
from ultralytics import YOLO
model = YOLO("TiBuDB_trained_weights/best_det_yolo26x_seed1000_baseline.pt")
results = model.predict("path/to/image.png")
RF-DETR
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")