add ultralytics

README.md

@@ -557,6 +557,7 @@ A collection of delicious docker recipes.
 - [x] traefik
 - [x] trinodb/trino
 - [x] ghcr.io/coqui-ai/tts-cpu
+- [x] ultralytics/ultralytics
 - [x] louislam/uptime-kuma
 - [x] pikvm/ustreamer :camera:
 - [x] mpromonet/v4l2rtspserver :camera:

ultralytics/README.md

@@ -0,0 +1,27 @@
+ultralytics
+===========
+
+[Ultralytics][1] creates cutting-edge, state-of-the-art (SOTA) YOLO models built on
+years of foundational research in computer vision and AI.
+
+```bash
+$ docker run --rm -it ultralytics/ultralytics:latest-arm64 python
+>>> from ultralytics import YOLO
+>>> model = YOLO("yolo11n.pt")
+>>> print(model.names)
+{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', ...}
+>>> results = model("https://ultralytics.com/images/bus.jpg")
+>>> for r in results: print(r.boxes.xywh)
+tensor([[400.0137, 478.8882, 792.3618, 499.0482],
+        [740.4135, 636.7728, 138.7925, 483.8793],
+        [143.3527, 651.8801, 191.8959, 504.6299],
+        [283.7633, 634.5621, 121.4087, 451.7472],
+        [ 34.4536, 714.2138,  68.8638, 316.2908]])
+```
+
+```bash
+$ docker run --rm -it ultralytics/ultralytics:latest-arm64 bash
+>>> yolo solutions count model=yolo11n.pt classes="[2,5,7]" source=https://basicai-asset.s3.amazonaws.com/www/blogs/yolov8-object-counting/street.mp4
+```
+
+[1]: https://github.com/ultralytics/ultralytics