Question 1

How to train a custom object detector with Detecto?

Accepted Answer

Use the Model class with your labels and Dataset; for example, model = Model(['dog', 'cat']) and model.fit(dataset) as shown in the transfer learning section. The README provides a 5-line code snippet for quick setup.

Question 2

Detecto vs YOLO for real-time detection?

Accepted Answer

Detecto uses PyTorch's Faster R-CNN by default, which is more accurate but slower than YOLO. For real-time needs, you can switch to lighter models like MobileNet via the model_name parameter, but it may still lag behind optimized YOLO implementations.

Question 3

Does Detecto support GPU acceleration?

Accepted Answer

Yes, since it's built on PyTorch, Detecto automatically leverages GPU if available and PyTorch is configured with CUDA. Ensure your PyTorch installation includes GPU support for best performance.

Question 4

How to save and load models in Detecto?

Accepted Answer

Use model.save('filename.pth') to serialize and Model.load('filename.pth') to reload, as demonstrated in the advanced usage section. This allows persistence of trained weights for reuse.

Question 5

Can Detecto handle live webcam feeds?

Accepted Answer

Yes, the visualize.detect_live(model) function runs inference on a live webcam, making it easy for real-time applications. The README includes an example with minimal code required.

Question 6

What are the system requirements for Detecto?

Accepted Answer

Detecto requires Python 3 and PyTorch dependencies; the README lists requirements.txt, but manual installation may be needed if pip fails. It's resource-intensive due to PyTorch, so adequate GPU memory is recommended.

Detecto - Train and run object detection models with 5-10 lines of code

What is Detecto - Train and run object detection models with 5-10 lines of code?

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