Question 1

How do I set up distributed training with TorchSeg?

Accepted Answer

Use torch.distributed.launch with the provided script, setting NGPUS to your GPU count. The framework spawns separate processes for each GPU, as detailed in the training section for efficient multi-GPU usage.

Question 2

TorchSeg vs MMsegmentation: which one should I use?

Accepted Answer

TorchSeg excels in fast, modular experimentation and distributed training, while MMsegmentation offers broader model support and better documentation. Choose TorchSeg for custom architectures and speed, MMsegmentation for a more comprehensive out-of-the-box solution.

Question 3

Can I use TorchSeg for real-time video segmentation?

Accepted Answer

Yes, the BiSeNet implementation is optimized for real-time inference. You can adapt the inference code for video streams, though performance may depend on hardware and latency requirements from the benchmarks.

Question 4

How to add a custom dataset to TorchSeg?

Accepted Answer

Create config files (train.txt, val.txt) with image and groundtruth paths separated by tabs, then modify config.py. The README outlines this in the training section, but you may need to handle dataset preprocessing manually.

Question 5

Does TorchSeg support TensorBoard for logging?

Accepted Answer

The README doesn't mention TensorBoard integration. You'll likely need to implement custom logging or rely on PyTorch's native tools, which could require additional coding effort.

Question 6

What's the performance difference between DFN and BiSeNet in TorchSeg?

Accepted Answer

DFN focuses on accuracy with higher IoU scores on datasets like Cityscapes, while BiSeNet prioritizes speed for real-time applications. Benchmarks in the README show DFN achieving ~80% IoU vs. BiSeNet's ~75% on similar backbones.

TorchSeg

What is TorchSeg?

Overview

Use Cases

Best For

Related Projects

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions