guides/data-collection-and-annotation/

[giscus[bot]]

guides/data-collection-and-annotation/

Data collection and annotation are vital steps in any computer vision project. Explore the tools, techniques, and best practices for collecting and annotating data.

https://docs.ultralytics.com/guides/data-collection-and-annotation/

[MoAbbasid]

Hello, Im looking for an open source annotation tool similar to labelme that exports directly to YOLOv8 format, something other than labelme?

[glenn-jocher]

@MoAbbasid hello! For an open-source annotation tool that exports directly to YOLOv8 format, you might want to try CVAT. It's quite versatile and supports exporting annotations directly into YOLO format among others. Another good option could be Label Studio, which offers flexibility in configuring export formats, including YOLO. Both tools provide robust features for efficient data labeling and are well-suited for various annotation tasks. Happy annotating! 😊

[MoAbbasid]

@glenn-jocher do they offer polygon annotations for instance segmentation?

[glenn-jocher]

Hello @MoAbbasid,

Yes, Ultralytics YOLOv8 does support polygon annotations for instance segmentation. This allows for more precise labeling of objects compared to traditional bounding boxes, which can be particularly useful for complex shapes and detailed segmentation tasks.

To create polygon annotations, you can use tools like Roboflow Annotate or CVAT. Both of these tools support polygon annotations and can export the data in a format compatible with YOLOv8.

Here's a brief overview of how you can use Roboflow for this purpose:

1. Create a Project: Start by creating a new project in Roboflow.
2. Upload Images: Upload your images to the project.
3. Annotate Images: Use the polygon tool to draw detailed outlines around objects. Roboflow also offers a SAM-based label assistant to speed up the annotation process.
4. Export Annotations: Once your annotations are complete, you can export the dataset in YOLOv8 format.

For more detailed steps on using Roboflow for data annotation and management, you can refer to the Roboflow documentation.

Here's a quick example of how to load and use a dataset with polygon annotations in YOLOv8:

from ultralytics import YOLO

# Load a YOLOv8 model
model = YOLO('yolov8n-seg.pt')

# Train the model on your custom dataset
model.train(data='path/to/your/dataset.yaml', epochs=100, imgsz=640)

# Validate the model
metrics = model.val()

# Make predictions
results = model.predict(source='path/to/your/images')

# Visualize results
results.show()

Feel free to reach out if you have any more questions or need further assistance. Happy annotating! 😊

[cawilchesp]

Hello, thanks for the info ! I would like to know how to perform active learning, specifically what are the metrics to choose the most informative images when the task is multi-instance multi-class object detection ?

[pderrenger]

@cawilchesp hello! Great question on active learning for multi-instance multi-class object detection. Active learning can significantly enhance your model by iteratively selecting the most informative samples for annotation. Here are some key metrics and strategies to consider:

Metrics for Selecting Informative Images:

1. Uncertainty Sampling: Choose images where the model is least confident. This can be measured using:

   • Entropy: High entropy indicates uncertainty in predictions.
   • Margin Sampling: Select samples where the difference between the top two class probabilities is smallest.
   • Least Confidence: Select samples with the lowest maximum class probability.

2. Diversity Sampling: Ensure a diverse set of samples to avoid redundancy.

   • Core-Set Selection: Use clustering techniques to select a diverse subset of data points.
   • Representative Sampling: Choose samples that represent the overall data distribution.

3. Error-Based Sampling: Focus on samples where the model previously made errors.

   • Misclassified Samples: Select images where the model's predictions were incorrect.
   • High Loss Samples: Choose samples with high loss values during training.

Implementing Active Learning:

Here's a basic example using uncertainty sampling with entropy:

import numpy as np
from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')

# Function to calculate entropy
def calculate_entropy(probs):
    return -np.sum(probs * np.log(probs), axis=1)

# Predict on a batch of images
results = model(['image1.jpg', 'image2.jpg', 'image3.jpg'])

# Calculate entropy for each image
entropies = [calculate_entropy(result.probs) for result in results]

# Select the image with the highest entropy
most_informative_image = results[np.argmax(entropies)]

Additional Resources:

For more detailed strategies and examples, you can refer to our Data Collection and Annotation Strategies.

Feel free to experiment with these methods and see which works best for your specific use case. If you have any further questions or need more examples, don't hesitate to ask! 😊