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README.md

@@ -0,0 +1,240 @@
+---
+license: agpl-3.0
+
+pipeline_tag: image-segmentation
+
+library_name: ultralytics
+
+base_model: "Ultralytics/YOLO26"
+
+tags:
+  - semantic-segmentation
+  - aerial-imagery
+  - drone
+  - uavid
+  - yolo26
+  - ultralytics
+  - pytorch
+  - computer-vision
+datasets:
+  - dronefreak/UAVid-2020
+metrics:
+  - miou
+  - pixel-accuracy
+---
+
+# YOLO26m-sem Finetuned on UAVid
+
+![License](https://img.shields.io/badge/License-AGPL--3.0-1f6feb?style=flat-square) ![Framework](https://img.shields.io/badge/Framework-Ultralytics-6a5acd?style=flat-square) ![Dataset](https://img.shields.io/badge/Dataset-UAVid-0aa1a7?style=flat-square) ![mIoU](https://img.shields.io/badge/mIoU-67.66%25-e8a33d?style=flat-square) ![Status](https://img.shields.io/badge/Status-Trained-2ea44f?style=flat-square) ![Maintained](https://img.shields.io/badge/Maintained-yes-17a2b8?style=flat-square)
+
+Fine-tuned YOLO26m semantic segmentation model for aerial UAV imagery using the UAVid benchmark dataset.
+
+This model is part of the **UAVid Semantic Segmentation Model Zoo**, a collection of CABiNet and YOLO26 models trained and evaluated under a common pipeline for aerial semantic segmentation.
+
+
+<p align="center">
+  <img src="uavid_showcase.gif" alt="UAVid Semantic Segmentation Demo">
+</p>
+
+---
+
+## Performance
+
+| Metric         | Score           |
+| -------------- | --------------- |
+| mIoU           | 67.66      |
+| Pixel Accuracy | 79.75 |
+| Parameters     | TODO    |
+| FLOPs          | TODO     |
+
+---
+
+## UAVid Model Zoo
+
+| Rank | Model                | mIoU (%)      | Pixel Acc (%)      | Params            | FLOPs            |
+| ---- | --------------------- | ------------- | ------------------ | ----------------- | ----------------- |
+| 1    | YOLO26m-sem | 67.66 | 79.75 | TODO | TODO |
+| 2    | YOLO26l-sem | 67.2 | 78.63 | TODO | TODO |
+| 3    | YOLO26s-sem | 66.88 | 79.0 | TODO | TODO |
+| 4    | YOLO26n-sem | 63.58 | 76.13 | TODO | TODO |
+| 5    | CABiNet (MobileNetV3-Large) | TBD | TBD | TODO | TODO |
+| 6    | CABiNet (MobileNetV3-Small) | TBD | TBD | TODO | TODO |
+| 7    | YOLO26x-sem | TBD | TBD | TODO | TODO |
+
+---
+
+## Per-Class IoU (%)
+
+| Class | YOLO26m-sem | YOLO26l-sem | YOLO26s-sem | YOLO26n-sem | CABiNet (MobileNetV3-Large) | CABiNet (MobileNetV3-Small) | YOLO26x-sem | 
+| --- | --- | --- | --- | --- | --- | --- | --- | 
+| Clutter | 65.3 | 66.0 | 63.3 | 60.9 | TBD | TBD | TBD | 
+| Building | 91.3 | 91.5 | 91.0 | 88.7 | TBD | TBD | TBD | 
+| Road | 79.2 | 80.4 | 77.5 | 76.8 | TBD | TBD | TBD | 
+| Static Car | 63.0 | 60.5 | 62.5 | 57.1 | TBD | TBD | TBD | 
+| Tree | 76.5 | 76.2 | 75.3 | 73.1 | TBD | TBD | TBD | 
+| Vegetation | 68.4 | 67.3 | 66.9 | 63.2 | TBD | TBD | TBD | 
+| Human | 31.3 | 29.7 | 30.5 | 25.2 | TBD | TBD | TBD | 
+| Moving Car | 66.3 | 66.0 | 66.5 | 63.3 | TBD | TBD | TBD | 
+
+---
+
+## Evaluation Visualizations
+
+### Per-Class IoU Bar Chart
+
+![IoU Bar Chart](iou_bar_chart.png)
+
+### Confusion Matrix
+
+![Confusion Matrix](confusion_matrix_normalized.png)
+
+### Loss Curves
+
+![Loss Curves](results.png)
+
+---
+
+## Dataset
+
+[UAVid](https://uavid.nl/) is a high-resolution UAV semantic segmentation benchmark of urban street scenes, captured from oblique aerial viewpoints along street-side flight paths.
+
+### Classes
+
+- Clutter
+- Building
+- Road
+- Static Car
+- Tree
+- Vegetation
+- Human
+- Moving Car
+
+---
+
+## Usage
+
+### Install Dependencies
+
+```bash
+pip install ultralytics huggingface_hub
+```
+
+### Load Model from Hugging Face
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+weights = hf_hub_download(
+    repo_id="dronefreak/yolo26m-sem-uavid",
+    filename="best.pt"
+)
+
+model = YOLO(weights)
+```
+
+### Run Inference
+
+```python
+results = model.predict(source="image.png", task="semantic", imgsz=1024)
+mask = results[0].semantic_mask.cpu().numpy().data  # (H, W) class-ID map
+```
+
+---
+
+## Training Configuration
+
+| Setting     | Value                                    |
+| ------------ | ------------------------------------------ |
+| Epochs       | 500                       |
+| Image size   | 1024                        |
+| Batch size   | 8                   |
+| Dataset      | UAVid (converted images/+masks/ format)    |
+| Framework    | Ultralytics YOLO                       |
+| cls_pw (class weighting) | 0.5          |
+
+---
+
+## Official Resources
+
+- **UAVid Semantic Segmentation Model Zoo:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo
+- **CABiNet repository:** https://github.com/dronefreak/CABiNet
+- **CABiNet Paper:** https://arxiv.org/abs/2011.00993v2
+- **Official UAVid Website:** https://uavid.nl/
+- **UAVid Dataset Archive:** https://doi.org/10.17026/dans-x9f-w9sa
+- **UAVid Paper:** https://arxiv.org/abs/1810.10438
+- **UAVid Published Journal:** https://doi.org/10.1016/j.isprsjprs.2020.05.009
+- **Ultralytics YOLO:** https://github.com/ultralytics/ultralytics
+- **Ultralytics YOLO26 Paper:** https://arxiv.org/abs/2606.03748
+
+---
+
+## Training Framework
+
+Trained with the [CABiNet repository](https://github.com/dronefreak/CABiNet), which pairs its own real-time segmentation trainer with a parallel Ultralytics YOLO26-sem pipeline — shared UAVid dataset tooling, training/eval, and mIoU benchmarking across both. Star the repo if you find these models useful!
+
+---
+
+## Known Limitations
+
+Performance may degrade in:
+
+* Very small or thin objects (e.g. pedestrians, moving cars at altitude)
+* Heavy occlusion under tree canopy
+* Motion blur on moving vehicles
+* Mixed/very high input resolutions (UAVid source images are 3840x2160 / 4096x2160; both pipelines evaluate at reduced imgsz)
+
+---
+
+## Citation
+
+Please cite the following:
+
+```bibtex
+
+@article{LYU2020108,
+    author = "Ye Lyu and George Vosselman and Gui-Song Xia and Alper Yilmaz and Michael Ying Yang",
+    title = "UAVid: A semantic segmentation dataset for UAV imagery",
+    journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
+    volume = "165",
+    pages = "108 - 119",
+    year = "2020",
+    issn = "0924-2716",
+    doi = "https://doi.org/10.1016/j.isprsjprs.2020.05.009",
+    url = "http://www.sciencedirect.com/science/article/pii/S0924271620301295",
+}
+
+@INPROCEEDINGS{9560977,
+  author={Kumaar, Saumya and Lyu, Ye and Nex, Francesco and Yang, Michael Ying},
+  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
+  title={CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation},
+  year={2021},
+  pages={13517-13524},
+  doi={10.1109/ICRA48506.2021.9560977}
+}
+
+@article{Kumaar_Real-time_Semantic_Segmentation_2021,
+author = {Kumaar, Saumya and Lyu, Ye and Nex, Francesco and Yang, Michael Ying},
+doi = {10.1016/j.isprsjprs.2021.06.006},
+journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
+pages = {124--134},
+title = {{Real-time Semantic Segmentation with Context Aggregation Network}},
+url = {https://www.sciencedirect.com/science/article/pii/S0924271621001647},
+volume = {178},
+year = {2021}
+}
+
+@article{jocher2026ultralytics,
+  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
+  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
+  journal={arXiv preprint arXiv:2606.03748},
+  year={2026}
+}
+
+@software{cabinet_uavid_benchmark,
+  author = {Kumaar, Saumya},
+  title = {CABiNet: Semantic Segmentation Benchmarking on UAVid (CABiNet vs. YOLO26)},
+  url = {https://github.com/dronefreak/CABiNet},
+  year = {2026}
+}
+```

args.yaml

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+task: semantic
+mode: train
+model: yolo26m-sem.pt
+data: /home/neural_debugger/projects/CABiNet/configs/dataset/uavid_yolo.yaml
+epochs: 500
+time: null
+patience: 50
+batch: 8
+imgsz: 1024
+save: true
+save_period: 50
+cache: false
+device: '0'
+workers: 8
+project: /home/neural_debugger/projects/CABiNet/experiments/yolo26m
+name: yolo26m
+exist_ok: false
+pretrained: true
+cls_remap: true
+optimizer: SGD
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: true
+close_mosaic: 15
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+quantize: null
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+distill_model: null
+dis: 6.0
+box: 7.5
+cls: 0.5
+cls_pw: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+rle: 1.0
+angle: 1.0
+nbs: 64
+hsv_h: 0.01
+hsv_s: 0.4
+hsv_v: 0.3
+degrees: 10.0
+translate: 0.05
+scale: 0.3
+shear: 0.0
+perspective: 0.0
+flipud: 0.2
+fliplr: 0.5
+bgr: 0.0
+mosaic: 0.8
+mixup: 0.1
+cutmix: 0.0
+copy_paste: 0.15
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: tracktrack.yaml
+save_dir: /home/neural_debugger/projects/CABiNet/experiments/yolo26m/yolo26m

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