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README.md

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+ 
+---
+tags:
+- ultralyticsplus
+- ultralytics
+- yolov8
+- yolo
+- vision
+- object-detection
+- pytorch
+library_name: ultralytics
+library_version: 8.0.4
+inference: false
+
+model-index:
+- name: ultralyticsplus/yolov8s
+  results:
+  - task:
+      type: object-detection
+
+    metrics:
+      - type: precision  # since mAP is not available on hf.co/metrics
+        value: 0.449  # min: 0.0 - max: 1.0
+        name: mAP
+---
+
+### Supported Labels
+
+```
+['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
+```
+
+
+### How to use
+
+- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
+
+```bash
+pip install -U ultralyticsplus==0.0.14
+```
+
+- Load model and perform prediction:
+
+```python
+from ultralyticsplus import YOLO, render_result
+
+# load model
+model = YOLO('ultralyticsplus/yolov8s')
+
+# set model parameters
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45  # NMS IoU threshold
+model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+# set image
+image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
+
+# perform inference
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+```
+

config.json

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+{"input_size": 640, "task": "object-detection", "best_ap": 0.449}

handler.py

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+from ultralyticsplus import YOLO
+from typing import Dict, Any, Optional, List
+from sahi import ObjectPrediction
+
+
+DEFAULT_CONFIG = {'conf': 0.25, 'iou': 0.45, 'agnostic_nms': False, 'max_det': 1000}
+
+
+class EndpointHandler():
+    def __init__(self):
+        self.model = YOLO('ultralyticsplus/yolov8s')
+
+    def __call__(self, image, config: Optional[Dict[str, Any]] = None) -> List[ObjectPrediction]:
+        """
+       data args:
+            image: image path to segment
+            config: (conf - NMS confidence threshold,
+                     iou - NMS IoU threshold,
+                     agnostic_nms - NMS class-agnostic: True / False,
+                     max_det - maximum number of detections per image)
+      Return:
+            object_predictions
+        """
+        if config is None:
+            config = DEFAULT_CONFIG
+        # Set model parameters
+        self.model.overrides['conf'] = config.get('conf')
+        self.model.overrides['iou'] = config.get('iou')
+        self.model.overrides['agnostic_nms'] = config.get('agnostic_nms')
+        self.model.overrides['max_det'] = config.get('max_det')   
+        
+        # perform inference
+        result = self.model.predict(image)[0]
+
+        names = self.model.model.names
+        boxes = result.boxes
+
+        object_predictions = []
+        if boxes is not None:
+            det_ind = 0
+            for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
+                object_prediction = ObjectPrediction(
+                    bbox=xyxy.tolist(),
+                    category_name=names[int(cls)],
+                    category_id=int(cls),
+                    score=conf,
+                )
+                object_predictions.append(object_prediction)
+                det_ind += 1
+
+        return object_predictions
+    

requirements.txt

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+ultralyticsplus==0.0.14

yolov8s.pt

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