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Tfoptflow

Tfoptflow is a TensorFlow implementation of PWC-Net, a neural network that predicts dense optical flow between consecutive image frames. Its code and notebooks support model training, evaluation, and inference on image pairs or datasets.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tfoptflow — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use Tfoptflow when a TensorFlow-based PWC-Net implementation is useful for training or inference, especially when predictions are needed for image pairs or an optical-flow dataset.

Who it is for

  • Computer-vision developers building motion-estimation workflows.
  • Researchers or practitioners who need to train, evaluate, or run PWC-Net optical-flow models.

Documented uses

  • Estimate pixel-level motion between two image frames.
  • Train or fine-tune a PWC-Net model using image-pair datasets.
  • Use optical-flow predictions as an input to tasks such as tracking, action recognition, or video object segmentation.
Before you choose

Requirements & limits

The repository is public and licensed under MIT. It provides Anaconda environment files for Ubuntu and Windows.

  • The implementation supports multi-GPU training and mixed-precision training.
  • The README says the code avoids dynamically loaded CUDA-based TensorFlow user operations and can run on supported TensorFlow hardware, including CPU-only configurations.
  • Pre-trained checkpoints are available in small and large variants with different parameter counts and model structures.
  • The README warns that its published GPU inference timings use unoptimized graphs and should not be treated as production performance estimates.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run PWC-Net inference on image pairs

Generate optical-flow predictions for pairs of images using a pre-trained PWC-Net model.

Before you start

  • A TensorFlow-capable environment; the README points to Anaconda3 v.5.2.0 environment files for Ubuntu and Windows.
  • A downloaded pre-trained model checkpoint.
  • Image pairs readable by skimage.io.imread.

Steps

  1. Prepare a list of image pairs, with each pair containing two consecutive frames.

  2. Set the inference device and controller, using a GPU device or /device:CPU:0 for CPU inference.

  3. Configure ModelPWCNet with the test options, checkpoint path, batch size, device settings, and model options matching the checkpoint.

  4. Pass the image-pair list to predict_from_img_pairs() to generate flow predictions.

Expected outcome

PWC-Net produces dense optical-flow predictions for the supplied image pairs.

Guide limitations

  • The README describes an example implementation rather than a verified run in this guide.
  • The pre-trained model must be downloaded separately; the README links to the model files.
  • Reported inference timing is for unoptimized graphs and is not presented as production performance.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
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Source availability
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Sources checked
17 October 2026
Original website title
philferriere/tfoptflow · Official homepage title · retained in its source language
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