This is the Linux app named Tracking Any Point (TAP) whose latest release can be downloaded as tapnetsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Tracking Any Point (TAP) with OnWorks for free.
Postępuj zgodnie z tymi instrukcjami, aby uruchomić tę aplikację:
- 1. Pobrałem tę aplikację na swój komputer.
- 2. Wpisz w naszym menedżerze plików https://www.onworks.net/myfiles.php?username=XXXXX z wybraną nazwą użytkownika.
- 3. Prześlij tę aplikację w takim menedżerze plików.
- 4. Uruchom emulator online OnWorks Linux lub Windows online lub emulator online MACOS z tej witryny.
- 5. W systemie operacyjnym OnWorks Linux, który właśnie uruchomiłeś, przejdź do naszego menedżera plików https://www.onworks.net/myfiles.php?username=XXXXX z wybraną nazwą użytkownika.
- 6. Pobierz aplikację, zainstaluj ją i uruchom.
ZRZUTY EKRANU
Ad
Tracking Any Point (TAP)
OPIS
TAPNet is the official Google DeepMind repository for Tracking Any Point (TAP), bundling datasets, models, benchmarks, and demos for precise point tracking in videos. The project includes the TAP-Vid and TAPVid-3D benchmarks, which evaluate long-range tracking of arbitrary points in 2D and 3D across diverse real and synthetic videos. Its flagship models—TAPIR, BootsTAPIR, and the latest TAPNext—use matching plus temporal refinement or next-token style propagation to achieve state-of-the-art accuracy and speed on TAP-Vid. RoboTAP demonstrates how TAPIR-style tracks can drive real-world robot manipulation via efficient imitation, and ships with a dataset of annotated robotics videos. The repo provides JAX and PyTorch checkpoints, Colab demos, and a real-time live demo that runs on a GPU to let you select and track points interactively.
Funkcjonalności
- Clear coordinate conventions and standardized metrics for fair, reproducible comparisons
- Training and evaluation pipelines, plus Kubric utilities for generating point tracks
- Colab notebooks and an offline/online real-time demo for quick experimentation
- RoboTAP benchmark and clustering demo for robotics manipulation from point tracks
- High-performance models including TAPIR, BootsTAPIR, and TAPNext with JAX and PyTorch checkpoints
- TAP-Vid and TAPVid-3D datasets and evaluation metrics for point tracking
Język programowania
Python, powłoka uniksowa
Kategorie
This is an application that can also be fetched from https://sourceforge.net/projects/tap.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
