Choosing a Computer Vision Library: PyTorch vs. TensorFlow vs. ONNX Runtime

Imagine you are a CV engineer at a startup

It's Monday morning. You need to prototype an object detector for a client demo by Friday. You've got a dataset of 2,500 images from a warehouse camera, and you need something that runs in real time on their edge device. You open your laptop and stare at the blank screen. Which framework do you pick?

We've all been there. The choice of deep learning framework is a fork in the road that affects every step from research to deployment. In this head-to-head, we'll compare PyTorch, TensorFlow, and ONNX Runtime across four criteria: ecosystem, training speed, deployment flexibility, and debugging ease. We'll argue that for most working practitioners, PyTorch is the default for research, but ONNX Runtime is the real winner when you need to ship. TensorFlow still has a place, but it's becoming the legacy choice.

PyTorch: The research darling

PyTorch's popularity stems from its Pythonic, imperative style. When you write a forward pass, you see the operations as they execute. That makes debugging a breeze—you can just print tensors or use pdb. The framework's flexibility is why most new papers release code in PyTorch first. For example, the Segment Anything Model (SAM) and the Vision Transformer (ViT) are both available in PyTorch, and their official implementations are the ones everyone uses (Segment Anything Model paper; Vision Transformer paper).

But PyTorch's strength in research doesn't automatically translate to production. While it has improved with TorchScript and TorchServe, deployment can still require extra steps. You often need to convert your model to another format for ONNX or TensorRT to get the best performance on edge devices. That said, PyTorch's ecosystem is unmatched: if you need a pre-trained model or a specific layer, you'll probably find it. The community is huge, and it's the default in most university courses (Computer Vision courses at Southampton/NTNU).

TensorFlow: The production veteran

TensorFlow has been around longer and was the first to offer a full production suite with TensorFlow Serving and TensorFlow Lite. It's used in many production systems, and the Keras API makes it easy to get started. But TensorFlow's graph-based execution can be frustrating for debugging. You write in Python, but the actual computation happens in a graph that's hard to inspect. This makes it less ideal for research, but it can be solid for deployment, especially on mobile via TFLite.

However, TensorFlow is losing its grip. Many teams are migrating to PyTorch or using JAX for research. TensorFlow's complexity and the fact that many modern models like Swin Transformer are first released in PyTorch (Swin Transformer paper) mean that if you want to stay on the cutting edge, you might be waiting longer for TensorFlow ports. For new projects, we'd recommend against starting fresh in TensorFlow unless you have a legacy system to support.

ONNX Runtime: The universal translator

ONNX Runtime is not a training framework—it's an inference engine that runs models in the ONNX format. It's like a universal translator for deep learning models. You can train in PyTorch or TensorFlow, export to ONNX, and then run the model on a wide range of hardware, from CPUs to GPUs to custom accelerators. The performance is often better than the original framework's runtime because it's optimized for inference.

For example, if you have a PyTorch model and you need to run it on an edge device, you can export to ONNX and then use ONNX Runtime with hardware-specific execution providers. This gives you the best of both worlds: the flexibility of PyTorch for training and the speed of ONNX Runtime for deployment. In our experience, you can get a 2-3x speedup on CPU compared to running the same model in PyTorch, simply by exporting to ONNX and using ONNX Runtime. Not to mention, ONNX Runtime is a lightweight dependency and easier to embed in C++ or mobile apps.

Head-to-Head Comparison

Which one should you choose?

Here's our rule of thumb: if you're doing research or prototyping, use PyTorch. It's the fastest way to test ideas and iterate. But when you need to deploy to production, especially on edge devices, export your model to ONNX and use ONNX Runtime. This gives you the best performance and portability. TensorFlow is only worth it if you're already invested in its ecosystem or need specific features like TensorFlow Lite for mobile—but even then, ONNX Runtime can often do the job better.

Let's make this concrete. Suppose you're building a real-time object detector for a security camera using YOLOv4. You train in PyTorch (or use a pre-trained model). For deployment, you export to ONNX. On the edge device (say an NVIDIA Jetson), you can run ONNX Runtime with the TensorRT execution provider, getting a significant speed boost. The YOLOv4 paper reports it runs at about 65 FPS on a Tesla V100 (YOLOv4 paper). On the Jetson, you might not get that, but you'll be much closer to real-time than if you ran the PyTorch model directly.

Quick tip: Before committing to a framework, check if the model architecture you need is available in that framework. If it's a recent paper, it's likely in PyTorch. If you need to deploy to a specific hardware, check if ONNX Runtime supports that hardware.

Warning: Don't assume that the framework you train with must be the framework you deploy with. It's common to train in PyTorch and deploy with ONNX Runtime.

The one thing to remember

The best tool is the one that gets you from idea to deployment with the least friction. For most of us, that means PyTorch for training and ONNX Runtime for inference. TensorFlow remains a fallback, but it's no longer the default. Make the switch if you haven't already.

Sources

• YOLOv4 paper - https://arxiv.org/abs/2004.10934
• Vision Transformer paper - https://arxiv.org/abs/2010.11929
• Segment Anything Model paper - https://arxiv.org/abs/2304.02643
• Computer Vision courses (Southampton / NTNU) - https://www.ntnu.edu/studies/courses/TDT4265