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Browse files- README.md +6 -5
- app.py +283 -0
- requirements.txt +9 -0
README.md
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---
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title: YOLO
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emoji:
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: YOLO-Playground
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emoji: π
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colorFrom: blue
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colorTo: green
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sdk: gradio
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sdk_version: 4.39.0
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app_file: app.py
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pinned: false
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license: mit
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short_description: Identify objects in images using multiple YOLO models
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from typing import Tuple
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import gradio as gr
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import numpy as np
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import supervision as sv
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from ultralytics import YOLO
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MARKDOWN = """
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<h1 style='text-align: left'>YOLO-Playground π</h1>
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Welcome to YOLO-Playground! This demo showcases the detection capabilities of various YOLO models pre-trained on the COCO Dataset. πππ
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A simple project just for fun for on the go object detection. π
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Inspired from YOLO-ARENA by SkalskiP. π
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- **YOLOv8**
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<div style="display: flex; align-items: center;">
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<a href="https://docs.ultralytics.com/models/yolov8/" style="margin-right: 10px;">
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<img src="https://badges.aleen42.com/src/github.svg">
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</a>
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<a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
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<img src="https://colab.research.google.com/assets/colab-badge.svg">
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</a>
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</div>
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- **YOLOv9**
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<div style="display: flex; align-items: center;">
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<a href="https://github.com/WongKinYiu/yolov9" style="margin-right: 10px;">
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<img src="https://badges.aleen42.com/src/github.svg">
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</a>
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<a href="https://arxiv.org/abs/2402.13616" style="margin-right: 10px;">
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<img src="https://img.shields.io/badge/arXiv-2402.13616-b31b1b.svg">
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</a>
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<a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
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<img src="https://colab.research.google.com/assets/colab-badge.svg">
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</a>
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</div>
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- **YOLOv10**
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<div style="display: flex; align-items: center;">
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<a href="https://github.com/THU-MIG/yolov10" style="margin-right: 10px;">
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<img src="https://badges.aleen42.com/src/github.svg">
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</a>
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<a href="https://arxiv.org/abs/2405.14458" style="margin-right: 10px;">
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<img src="https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg">
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</a>
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<a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
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<img src="https://colab.research.google.com/assets/colab-badge.svg">
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</a>
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</div>
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- **YOLO11**
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<div style="display: flex; align-items: center;">
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<a href="https://docs.ultralytics.com/models/yolo11/" style="margin-right: 10px;">
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<img src="https://badges.aleen42.com/src/github.svg">
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</a>
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<a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
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<img src="https://colab.research.google.com/assets/colab-badge.svg">
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</a>
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</div>
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Powered by Roboflow [Inference](https://github.com/roboflow/inference),
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[Supervision](https://github.com/roboflow/supervision) and [Ultralytics](https://github.com/ultralytics/ultralytics).π₯
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"""
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IMAGE_EXAMPLES = [
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['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 0.3, 0.3, 0.3, 0.5],
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['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.3, 0.3, 0.5],
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['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.3, 0.3, 0.5],
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]
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YOLO_V8S_MODEL = YOLO("yolov8s.pt")
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YOLO_V9S_MODEL = YOLO("yolov9s.pt")
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YOLO_V10S_MODEL = YOLO("yolov10s.pt")
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YOLO_11S_MODEL = YOLO("yolo11s.pt")
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LABEL_ANNOTATORS = sv.LabelAnnotator()
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BOUNDING_BOX_ANNOTATORS = sv.BoxAnnotator()
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def detect_and_annotate(
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model,
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input_image: np.ndarray,
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confidence_threshold: float,
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iou_threshold: float,
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class_id_mapping: dict = None
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) -> np.ndarray:
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result = model(
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input_image,
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conf=confidence_threshold,
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iou=iou_threshold
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)[0]
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detections = sv.Detections.from_ultralytics(result)
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if class_id_mapping:
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detections.class_id = np.array([
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class_id_mapping[class_id]
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for class_id
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in detections.class_id
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])
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labels = [
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f"{class_name} ({confidence:.2f})"
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for class_name, confidence
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in zip(detections['class_name'], detections.confidence)
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]
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annotated_image = input_image.copy()
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annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(
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scene=annotated_image, detections=detections)
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annotated_image = LABEL_ANNOTATORS.annotate(
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scene=annotated_image, detections=detections, labels=labels)
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return annotated_image
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def process_image(
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input_image: np.ndarray,
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yolo_v8_confidence_threshold: float,
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yolo_v9_confidence_threshold: float,
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yolo_v10_confidence_threshold: float,
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yolov11_confidence_threshold: float,
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iou_threshold: float
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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# Validate iou_threshold before using it
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if iou_threshold is None or not isinstance(iou_threshold, float):
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iou_threshold = 0.3 # Default value, adjust as necessary
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yolo_v8s_annotated_image = detect_and_annotate(
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YOLO_V8S_MODEL, input_image, yolo_v8_confidence_threshold, iou_threshold)
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yolo_v9s_annotated_image = detect_and_annotate(
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YOLO_V9S_MODEL, input_image, yolo_v9_confidence_threshold, iou_threshold)
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yolo_v10s_annotated_image = detect_and_annotate(
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YOLO_V10S_MODEL, input_image, yolo_v10_confidence_threshold, iou_threshold)
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yolo_11s_annnotated_image = detect_and_annotate(
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YOLO_11S_MODEL, input_image, yolov11_confidence_threshold, iou_threshold)
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return (
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yolo_v8s_annotated_image,
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yolo_v9s_annotated_image,
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yolo_v10s_annotated_image,
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yolo_11s_annnotated_image
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)
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yolo_v8s_confidence_threshold_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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value=0.3,
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step=0.01,
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label="YOLOv8s Confidence Threshold",
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info=(
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"The confidence threshold for the YOLO model. Lower the threshold to "
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"reduce false negatives, enhancing the model's sensitivity to detect "
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"sought-after objects. Conversely, increase the threshold to minimize false "
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"positives, preventing the model from identifying objects it shouldn't."
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))
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yolo_v9s_confidence_threshold_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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value=0.3,
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step=0.01,
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label="YOLOv9s Confidence Threshold",
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info=(
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"The confidence threshold for the YOLO model. Lower the threshold to "
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"reduce false negatives, enhancing the model's sensitivity to detect "
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"sought-after objects. Conversely, increase the threshold to minimize false "
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"positives, preventing the model from identifying objects it shouldn't."
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))
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yolo_v10s_confidence_threshold_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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value=0.3,
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step=0.01,
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label="YOLOv10s Confidence Threshold",
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info=(
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"The confidence threshold for the YOLO model. Lower the threshold to "
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"reduce false negatives, enhancing the model's sensitivity to detect "
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"sought-after objects. Conversely, increase the threshold to minimize false "
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"positives, preventing the model from identifying objects it shouldn't."
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))
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yolo_11s_confidence_threshold_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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value=0.3,
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step=0.01,
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label="YOLO11s Confidence Threshold",
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info=(
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"The confidence threshold for the YOLO model. Lower the threshold to "
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"reduce false negatives, enhancing the model's sensitivity to detect "
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"sought-after objects. Conversely, increase the threshold to minimize false "
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"positives, preventing the model from identifying objects it shouldn't."
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))
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iou_threshold_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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value=0.5,
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step=0.01,
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label="IoU Threshold",
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info=(
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"The Intersection over Union (IoU) threshold for non-maximum suppression. "
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"Decrease the value to lessen the occurrence of overlapping bounding boxes, "
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"making the detection process stricter. On the other hand, increase the value "
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"to allow more overlapping bounding boxes, accommodating a broader range of "
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"detections."
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))
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with gr.Blocks() as demo:
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gr.Markdown(MARKDOWN)
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with gr.Accordion("Configuration", open=False):
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with gr.Row():
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yolo_v8s_confidence_threshold_component.render()
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yolo_v9s_confidence_threshold_component.render()
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yolo_v10s_confidence_threshold_component.render()
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yolo_11s_confidence_threshold_component.render()
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iou_threshold_component.render()
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with gr.Row():
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input_image_component = gr.Image(
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type='pil',
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label='Input'
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)
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with gr.Row():
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yolo_v8s_output_image_component = gr.Image(
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type='pil',
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label='YOLOv8s'
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)
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yolo_v9s_output_image_component = gr.Image(
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type='pil',
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label='YOLOv9s'
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)
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with gr.Row():
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yolo_v10s_output_image_component = gr.Image(
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type='pil',
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label='YOLOv10s'
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)
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yolo_11s_output_image_component = gr.Image(
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type='pil',
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label='YOLO11s'
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)
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submit_button_component = gr.Button(
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value='Submit',
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scale=1,
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variant='primary'
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)
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gr.Examples(
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fn=process_image,
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examples=IMAGE_EXAMPLES,
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inputs=[
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input_image_component,
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yolo_v8s_confidence_threshold_component,
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yolo_v9s_confidence_threshold_component,
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yolo_v10s_confidence_threshold_component,
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yolo_11s_confidence_threshold_component,
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iou_threshold_component
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],
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outputs=[
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yolo_v8s_output_image_component,
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yolo_v9s_output_image_component,
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yolo_v10s_output_image_component,
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261 |
+
yolo_11s_output_image_component
|
262 |
+
]
|
263 |
+
)
|
264 |
+
|
265 |
+
submit_button_component.click(
|
266 |
+
fn=process_image,
|
267 |
+
inputs=[
|
268 |
+
input_image_component,
|
269 |
+
yolo_v8s_confidence_threshold_component,
|
270 |
+
yolo_v9s_confidence_threshold_component,
|
271 |
+
yolo_v10s_confidence_threshold_component,
|
272 |
+
yolo_11s_confidence_threshold_component,
|
273 |
+
iou_threshold_component
|
274 |
+
],
|
275 |
+
outputs=[
|
276 |
+
yolo_v8s_output_image_component,
|
277 |
+
yolo_v9s_output_image_component,
|
278 |
+
yolo_v10s_output_image_component,
|
279 |
+
yolo_11s_output_image_component
|
280 |
+
]
|
281 |
+
)
|
282 |
+
|
283 |
+
demo.launch(debug=False, show_error=True, max_threads=1)
|
requirements.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
setuptools<70.0.0
|
2 |
+
awscli==1.29.54
|
3 |
+
gradio
|
4 |
+
pydantic==2.10.6
|
5 |
+
inference
|
6 |
+
supervision
|
7 |
+
ultralytics
|
8 |
+
dill
|
9 |
+
timm
|