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README.md

@@ -0,0 +1,242 @@
+---
+license: agpl-3.0
+
+pipeline_tag: image-segmentation
+
+library_name: ultralytics
+
+base_model: "Ultralytics/YOLO26"
+
+tags:
+  - semantic-segmentation
+  - aerial-imagery
+  - drone
+  - aeroscapes
+  - yolo26
+  - ultralytics
+  - pytorch
+  - computer-vision
+datasets:
+  - dronefreak/Aeroscapes
+metrics:
+  - miou
+  - pixel-accuracy
+---
+
+# YOLO26n-sem Finetuned on AeroScapes
+
+![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-AeroScapes-0aa1a7?style=flat-square) ![mIoU](https://img.shields.io/badge/mIoU-64.86%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 YOLO26n semantic segmentation model for aerial drone imagery using the AeroScapes benchmark dataset.
+
+This model is part of the **AeroScapes 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="demo_grid.png" alt="YOLO26n-sem on AeroScapes: Input / Ground Truth / Prediction">
+</p>
+
+<sub>Qualitative results on AeroScapes val-split examples — single-scale (imgsz=896) inference, no TTA.</sub>
+
+---
+
+## Performance
+
+| Metric              | Score           |
+| ------------------- | --------------- |
+| mIoU                | 64.86      |
+| Pixel Accuracy      | 92.84 |
+| Parameters (M)      | 1.63    |
+| FLOPs (GFLOPs @ 720px) | 5.6     |
+
+---
+
+## AeroScapes Model Zoo
+
+| Rank | Model                | mIoU (%)      | Pixel Acc (%)      | Params (M)        | FLOPs (GFLOPs)    |
+| ---- | --------------------- | ------------- | ------------------ | ----------------- | ----------------- |
+| 1    | YOLO26x-sem | 68.36 | 93.91 | 40.15 | 213.0 |
+| 2    | YOLO26l-sem | 68.0 | 93.16 | 17.86 | 95.1 |
+| 3    | CABiNet (MobileNetV3-Large) | 67.83 | 94.32 | 9.18 | 27.4 |
+| 4    | YOLO26m-sem | 66.97 | 93.51 | 14.31 | 75.2 |
+| 5    | YOLO26s-sem | 65.44 | 93.22 | 6.50 | 21.9 |
+| 6    | YOLO26n-sem | 64.86 | 92.84 | 1.63 | 5.6 |
+
+---
+
+## Per-Class IoU (%)
+
+| Class | YOLO26x-sem | YOLO26l-sem | CABiNet (MobileNetV3-Large) | YOLO26m-sem | YOLO26s-sem | YOLO26n-sem | 
+| --- | --- | --- | --- | --- | --- | --- | 
+| Background | 81.69 | 79.43 | 82.67 | 80.75 | 79.31 | 78.43 | 
+| Person | 53.34 | 49.28 | 46.9 | 50.81 | 49.44 | 44.51 | 
+| Bike | 35.61 | 42.18 | 42.35 | 38.4 | 30.09 | 35.7 | 
+| Car | 86.16 | 87.67 | 85.08 | 84.76 | 84.54 | 85.28 | 
+| Drone | 60.46 | 65.97 | 61.77 | 58.91 | 59.73 | 50.88 | 
+| Boat | 82.04 | 79.76 | 77.67 | 80.33 | 76.19 | 80.28 | 
+| Animal | 41.21 | 37.08 | 33.74 | 38.22 | 36.21 | 37.15 | 
+| Obstacle | 26.91 | 29.17 | 23.13 | 24.14 | 19.8 | 17.29 | 
+| Construction | 75.76 | 67.09 | 78.81 | 72.87 | 71.02 | 74.39 | 
+| Vegetation | 94.07 | 93.6 | 94.3 | 93.99 | 93.93 | 93.55 | 
+| Road | 92.14 | 92.07 | 92.91 | 91.94 | 91.64 | 89.1 | 
+| Sky | 90.96 | 92.72 | 94.57 | 88.57 | 93.35 | 91.73 | 
+
+---
+
+## 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
+
+[AeroScapes](https://github.com/ishann/aeroscapes) is an aerial semantic segmentation benchmark of ~3,200 720p images captured by a fleet of drones across roughly 10 flight sequences, covering urban and suburban scenes with people, vehicles, and small objects.
+
+### Classes
+
+- Background
+- Person
+- Bike
+- Car
+- Drone
+- Boat
+- Animal
+- Obstacle
+- Construction
+- Vegetation
+- Road
+- Sky
+
+---
+
+## 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/aeroscapes-yolo26n-sem",
+    filename="best.pt"
+)
+
+model = YOLO(weights)
+```
+
+### Run Inference
+
+```python
+results = model.predict(source="image.png", task="semantic", imgsz=896)
+mask = results[0].semantic_mask.cpu().numpy().data  # (H, W) class-ID map
+```
+
+---
+
+## Training Configuration
+
+| Setting     | Value                                    |
+| ------------ | ------------------------------------------ |
+| Epochs       | 120                       |
+| Image size   | 896                        |
+| Batch size   | 8                   |
+| Dataset      | AeroScapes (converted images/+masks/ format)    |
+| Framework    | Ultralytics YOLO                       |
+| cls_pw (class weighting) | 0.5          |
+
+---
+
+## Official Resources
+
+- **AeroScapes Semantic Segmentation Model Zoo:** https://huggingface.co/collections/dronefreak/aeroscapes-semantic-segmentation-model-zoo
+- **CABiNet repository:** https://github.com/dronefreak/CABiNet
+- **CABiNet Paper:** https://arxiv.org/abs/2011.00993v2
+- **AeroScapes Dataset (Hugging Face):** https://huggingface.co/datasets/dronefreak/Aeroscapes
+- **AeroScapes Repository:** https://github.com/ishann/aeroscapes
+- **AeroScapes Paper (CMU PDF):** https://www.cs.cmu.edu/~deva/papers/aeroscapes.pdf
+- **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 dataset tooling, training/eval, and mIoU benchmarking across UAVid, AeroScapes, and VDD. Star the repo if you find these models useful!
+
+---
+
+## Known Limitations
+
+Performance may degrade in:
+
+* Very rare classes (Bike, Drone, Boat) each cover well under 2% of any single frame in the val split — expect unreliable predictions for these
+* No official test split — all reported numbers are on the val split, unlike UAVid/VDD
+* Frames are drawn from only ~10 flight sequences, so scene diversity is lower than UAVid/VDD despite the image count
+* Native resolution (1280x720) downsampled to the eval imgsz — fine detail on small objects (pedestrians, bikes) can be lost
+
+---
+
+## Citation
+
+Please cite the following:
+
+```bibtex
+@inproceedings{nigam2018ensemble,
+  title={Ensemble Knowledge Transfer for Semantic Segmentation},
+  author={Nigam, Ishan and Huang, Chen and Ramanan, Deva},
+  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
+  year={2018}
+}
+
+@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: yolo26n-sem.pt
+data: /home/saumya.saksena/projects/CABiNet/configs/dataset/aeroscapes_yolo.yaml
+epochs: 300
+time: null
+patience: 50
+batch: 8
+imgsz: 720
+save: true
+save_period: 50
+cache: false
+device: '0'
+workers: 8
+project: /home/saumya.saksena/projects/CABiNet/experiments/aeroscapes_yolo
+name: yolo26n
+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/saumya.saksena/projects/CABiNet/experiments/aeroscapes_yolo/yolo26n

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