feat: 🚀 add YOLOv8s WorldV2 deployment workflow and model predictor

.github/workflows/push.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        model: [yolo11n]
+        model: [yolo11n,yolov8s-worldv2]
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -31,7 +31,7 @@ jobs:
         uses: replicate/setup-cog@v2
         with:
           token: ${{ secrets.REPLICATE_API_TOKEN }}
-      - name: Download YOLO11n weights
+      - name: Download model weights
         run: python ${{ matrix.model }}/download.py
       - name: Build model image
         working-directory: ${{ matrix.model }}

yolov8s-worldv2/README.md

@@ -0,0 +1,30 @@
+# YOLOv8s WorldV2 Demo Deployment
+
+Deploy the official YOLOv8s WorldV2 model to Replicate with PyTorch inference at https://replicate.com/ultralytics/yolov8s-worldv2.
+
+## Setup
+
+1. **Deploy to Replicate:**
+
+   ```bash
+   cog push r8.im/ultralytics/yolov8s-worldv2
+   ```
+
+## Model Details
+
+- **Model**: YOLOv8s WorldV2 (Small)
+- **Parameters**: 12.7M
+- **Format**: PyTorch (.pt)
+- **Use Case**: Demonstration of official Ultralytics model deployment
+
+## Model Files
+
+**Note:** The model weights (`yolov8s-worldv2.pt`) will be automatically downloaded by ultralytics when the container starts.
+
+## Configuration
+
+- **GPU**: Disabled by default (CPU inference)
+- **Python**: 3.12 with PyTorch 2.3.1+
+- **Framework**: Ultralytics 8.3+
+- **Input**: Single image with configurable confidence/IoU thresholds
+- **Output**: Annotated image with detected objects

yolov8s-worldv2/cog.yaml

@@ -0,0 +1,15 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+build:
+  gpu: false
+  python_version: "3.12"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+    - "ffmpeg"
+  python_packages:
+    - "ultralytics>=8.3.0"
+    - "clip @ git+https://github.com/ultralytics/CLIP.git@main#egg=clip"
+
+predict: predict.py:Predictor
+image: r8.im/ultralytics/yolov8s-worldv2

yolov8s-worldv2/download.py

@@ -0,0 +1,20 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from pathlib import Path
+
+from ultralytics import YOLOWorld
+
+
+def main():
+    """Download YOLOv8s-worldv2 weights and move to model directory."""
+    current_dir = Path(__file__).parent
+    YOLOWorld(current_dir / "yolov8s-worldv2.pt")
+
+    # List files in model directory
+    print(f"Files in {current_dir.name} directory:")
+    for file in sorted(current_dir.iterdir()):
+        print(f"  {file.stat().st_size:>10} {file.name}")
+
+
+if __name__ == "__main__":
+    main()

yolov8s-worldv2/predict.py

@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from typing import Optional
+
+from cog import BaseModel, BasePredictor, Input, Path
+from ultralytics import YOLOWorld
+
+
+class Output(BaseModel):
+    """Output model for predictions."""
+
+    image: Optional[Path] = None
+    json_str: Optional[str] = None
+
+
+class Predictor(BasePredictor):
+    """YOLOv8s WorldV2 model predictor for Replicate deployment."""
+
+    def setup(self) -> None:
+        """Load YOLOWorld model into memory."""
+        self.model = YOLOWorld("yolov8s-worldv2.pt")
+
+    def re_init_model(self, class_names: str) -> None:
+        """Re-Initialize model with class names."""
+        self.model = YOLOWorld("yolov8s-worldv2.pt")
+        class_list = class_names.split(", ")
+        self.model.set_classes(class_list)
+
+    def predict(
+        self,
+        image: Path = Input(description="Input image"),
+        conf: float = Input(description="Confidence threshold", default=0.25, ge=0.0, le=1.0),
+        iou: float = Input(description="IoU threshold for NMS", default=0.45, ge=0.0, le=1.0),
+        imgsz: int = Input(description="Image size", default=640, choices=[320, 416, 512, 640, 832, 1024, 1280]),
+        class_names: str = Input(
+            description="Comma-separated list of class names to filter results (e.g., 'person, bus, sign') You can also leave it empty to detect classes automatically.",
+            default="person, bus, sign",
+        ),
+        return_json: bool = Input(description="Return detection results as JSON", default=False),
+    ) -> Output:
+        """Run inference and return annotated image with optional JSON results."""
+        self.re_init_model(class_names)
+        result = self.model(str(image), conf=conf, iou=iou, imgsz=imgsz)[0]
+        image_path = "output.png"
+        result.save(image_path)
+
+        if return_json:
+            return Output(image=Path(image_path), json_str=result.to_json())
+        else:
+            return Output(image=Path(image_path))