Update README.md

README.md

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- ---
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+---
 license: etalab-2.0
-
-
+---
 ## 📦 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 (basesize) | N/A | 
-
 | **Segmentation**| YOLO26x | `best_seg_yolo26x_seed100_baseline.pt` | Baseline (1x) | 128 | 128 |
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 | **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 (basesize) | N/A | 
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 | **OBB** | YOLO26x | `best_obb_yolo26x_seed5000_baseline.pt` | Oriented Bbox (1x) | 128 | 512 |
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 | **OBB** | YOLO26x | `best_obb_yolo26x_seed5000_x4.pt` | Oriented Bbox (4x) | 128 | 512 |
 
-
 ### 🛠️ Quick Load Example (Ultralytics)
 
 ```python
+from ultralytics import YOLO
+model = YOLO("TiBuDB_trained_weights/best_det_yolo26x_seed1000_baseline.pt")
+results = model.predict("d---
+license: etalab-2.0
+---
+
+## Model Weights
+
+The trained weights for all benchmarks are hosted on [Hugging Face](https://huggingface.co/datasets/anonymous-submission-dataset-code/TiBuDB).
+
+### 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")
+```
 
-results = model.predict("data/tibudb_dataset/yolo_det/val/images/test_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")ata/tibudb_dataset/yolo_det/val/images/test_image.png")
+---