Update README.md

README.md

@@ -1,31 +1,6 @@
 ---
 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 |
-| **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 | 
-| **OBB** | YOLO26x | `best_obb_yolo26x_seed5000_baseline.pt` | Oriented Bbox (1x) | 128 | 512 |
-| **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
 
@@ -65,5 +40,5 @@ results = model.predict("path/to/image.png")
 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")
----
+results = model.predict("path/to/image.png")
+```