datasets/detect/visdrone/

[giscus[bot]]

datasets/detect/visdrone/

Explore the VisDrone Dataset, a large-scale benchmark for drone-based image analysis, and learn how to train a YOLO model using it.

https://docs.ultralytics.com/datasets/detect/visdrone/

[Elvis-Kiilu2]

This is nice, but what's next after the detection? How do we send/retrieve the data to a given database? What further insights can we draw from the detected data? What other technologies can we intergrate to this to draw meaningful inferences??

[pderrenger]

After detection, you have a few options to extend the functionality:

1. Data Storage: To send and store detections on a database:

   import sqlite3
   
   # Connect to SQLite database
   connection = sqlite3.connect('my_detections.db')
   cursor = connection.cursor()
   
   # Create table
   cursor.execute('''CREATE TABLE IF NOT EXISTS detections
                     (timestamp DATETIME, class TEXT, confidence REAL, x REAL, y REAL, width REAL, height REAL)''')
   
   # Example insertion from detection
   detections = results.boxes
   timestamp = datetime.now()
   for det in detections:
      cursor.execute('''INSERT INTO detections (timestamp, class, confidence, x, y, width, height)
                        VALUES (?, ?, ?, ?, ?, ?, ?)''',
                         (timestamp, names[det.cls], det.conf, *det.xywh))
   
   connection.commit()
   connection.close()

2. Further Analysis: Analyze the time series of detections to observe trends, detect anomalies, or aggregate counts over time.

3. Integration with Other Technologies: Integrate with IoT platforms for real-time alerts or incorporate AI-based services for enhanced image analysis and decision-making processes.

These steps help bridge the gap between raw detection outputs and actionable insights or responses in real-world applications.

[Elvis-Kiilu2]

Thanks @pderrenger for the reply. I wanted to implement this for my next project. Your insights are a good starting point. My aim is to extract/read data from the detected class/boxes. Then compare and match the read data with with those in a database that in a cloud data warehouse, Bigquery to be specific.

[pderrenger]

Hey there! It's great to hear you're planning to use YOLOv8 for your project. To extract and compare data from detected objects with your BigQuery database, you can follow these steps:

1. Extract Data: After detection, extract the necessary details from each detection (like class, confidence, and bounding box coordinates).

2. Format Data: Convert this data into a suitable format (like JSON or CSV) that can be easily ingested into BigQuery.

3. Upload to BigQuery: Use the BigQuery API to upload your formatted data. Here’s a small Python snippet using google-cloud-bigquery:

   from google.cloud import bigquery
   
   # Construct a BigQuery client object.
   client = bigquery.Client()
   
   # Define your table ID (dataset.table)
   table_id = "your-project.your_dataset.your_table"
   
   # Define the data to be inserted
   rows_to_insert = [
       {u"class": detected_class, u"confidence": confidence, u"x_min": x_min, u"y_min": y_min, u"width": width, u"height": height}
   ]
   
   # Insert data into BigQuery
   errors = client.insert_rows_json(table_id, rows_to_insert)
   if errors == []:
       print("New rows have been added.")

4. Query and Analyze: Once your data is in BigQuery, you can use SQL queries to compare, analyze, and draw insights based on your specific needs.

This workflow will help you leverage the power of cloud computing to analyze large datasets efficiently. If you need further assistance or have more specific requirements, feel free to ask! 🚀

[mariem158]

quelles sont les étapes pour télécharger roboflow100 sur google colab. je veux utiliser ce dataset pour faire la détection précise des véhicules filmés par des caméras ?

[pderrenger]

@mariem158 bonjour!

Pour télécharger et utiliser le dataset Roboflow100 sur Google Colab pour la détection précise des véhicules, vous pouvez suivre les étapes ci-dessous. Cependant, il semble que vous ayez mentionné le dataset VisDrone dans votre commentaire, donc je vais également inclure des informations sur ce dataset.

Étapes pour télécharger Roboflow100 sur Google Colab

1. Installer les dépendances nécessaires:

   !pip install roboflow

2. Importer et configurer Roboflow:

   from roboflow import Roboflow
   rf = Roboflow(api_key="YOUR_API_KEY")
   project = rf.workspace().project("roboflow100")
   dataset = project.version("1").download("yolov8")

3. Configurer le dataset pour l'entraînement:

   from ultralytics import YOLO
   
   # Charger un modèle pré-entraîné
   model = YOLO("yolov8n.pt")
   
   # Entraîner le modèle
   results = model.train(data=dataset.location + "/data.yaml", epochs=100, imgsz=640)

Utilisation du dataset VisDrone

Le dataset VisDrone est une excellente ressource pour la détection d'objets, le suivi d'objets et le comptage de foules dans des images et vidéos capturées par des drones. Voici comment vous pouvez l'utiliser avec YOLOv8:

1. Télécharger et configurer le dataset VisDrone:

   from ultralytics import YOLO
   
   # Charger un modèle pré-entraîné
   model = YOLO("yolov8n.pt")
   
   # Entraîner le modèle
   results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)

2. YAML de configuration pour VisDrone:

   # Ultralytics YOLO 🚀, AGPL-3.0 license
   # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
   path: ../datasets/VisDrone
   train: VisDrone2019-DET-train/images
   val: VisDrone2019-DET-val/images
   test: VisDrone2019-DET-test-dev/images
   names:
     0: pedestrian
     1: people
     2: bicycle
     3: car
     4: van
     5: truck
     6: tricycle
     7: awning-tricycle
     8: bus
     9: motor

Pour plus de détails sur l'utilisation du dataset VisDrone, vous pouvez consulter la documentation complète ici : VisDrone Dataset Documentation.

N'hésitez pas à revenir vers nous si vous avez d'autres questions! 😊

Cordialement,
L'équipe Ultralytics

[SudhanshuBlaze]

I trained yolov10x on VisDrone dataset with the below parameters, but I got the final box_loss of 1.97. I feel that model performed very poorly, do you know what might be the issue? Or what hyperparameters should I use?

from ultralytics import YOLO

# Load a model
model = YOLO("yolov10x.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="VisDrone.yaml", epochs=1000, imgsz=640, patience=250, project='visdrone', name='train', batch=-1)

[glenn-jocher]

@SudhanshuBlaze it sounds like your model might be struggling with the complexity of the VisDrone dataset. First, ensure you're using the latest version of the Ultralytics package. You might also want to experiment with different learning rates, batch sizes, and augmentation techniques. Sometimes, reducing the learning rate or increasing the batch size can help stabilize training. Additionally, consider using early stopping to prevent overfitting. If the issue persists, reviewing the dataset for any inconsistencies or mislabeled data could also be beneficial. For more detailed guidance, you can refer to the VisDrone documentation.

[lawyeh]

Hello, the example and the yaml file pull from the object detection in images dataset (task 1). How can I train the model for object detection in video (task )?

[glenn-jocher]

For video object detection (Task 2) with VisDrone, you'll use the same YOLO training process but process video frames as individual images. Ensure your dataset paths in VisDrone.yaml point to the video task's frame sequences (organized as images), then train normally. Video inference can be done with model.predict(source='video.mp4') after training. For video-specific annotations, verify they're converted to YOLO format per frame.