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 # Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
-A general python framework for visual object tracking and video object segmentation, based on **PyTorch**.
 
-### New version released!
-* Code for our CVPR 2020 paper [Probabilistic Regression for Visual Tracking](https://arxiv.org/abs/2003.12565).  
-* Tools for analyzing results: performance metrics, plots, tables, etc.  
-* Support for multi-object tracking. Any tracker can be run in multi-object mode.    
-* Support for Video Object Segmentation (**VOS**): training, datasets, evaluation, etc.  
-* Code for [Learning What to Learn for Video Object Segmentation](https://arxiv.org/abs/2003.11540) will be released soon.  
-* Much more...   
 
-**Note:** Many of our changes are breaking. Integrate your extensions into the new version of PyTracking should not be difficult.
-We advise to check the updated implementation and train scripts of DiMP in order to update your code.
-
-## Highlights
-
-
-
-Libraries for implementing and evaluating visual trackers. It includes
-
-* All common **tracking** and **video object segmentation** datasets.  
-* Scripts to **analyse** tracker performance and obtain standard performance scores.
-* General building blocks, including **deep networks**, **optimization**, **feature extraction** and utilities for **correlation filter** tracking.  
-
-### [Training Framework: LTR](ltr)
-
-**LTR** (Learning Tracking Representations) is a general framework for training your visual tracking networks. It is equipped with
-
-* All common **training datasets** for visual object tracking and segmentation.  
-* Functions for data **sampling**, **processing** etc.  
-* Network **modules** for visual tracking.
-* And much more...
-
-
-## Tracker
-The toolkit contains the implementation of the following trackers.  
-
-
-
-## [Model Zoo](MODEL_ZOO.md)
-The tracker models trained using PyTracking, along with their results on standard tracking 
-benchmarks are provided in the [model zoo](MODEL_ZOO.md). 
-
-
-## Installation
-
-#### Clone the GIT repository.  
-```bash
-git clone https://github.com/visionml/pytracking.git
-```
-
-#### Clone the submodules.  
-In the repository directory, run the commands:  
-```bash
-git submodule update --init  
-```  
-#### Install dependencies
-Run the installation script to install all the dependencies. You need to provide the conda install path (e.g. ~/anaconda3) and the name for the created conda environment (here ```pytracking```).  
-```bash
-bash install.sh conda_install_path pytracking
-```  
-This script will also download the default networks and set-up the environment.  
-
-**Note:** The install script has been tested on an Ubuntu 18.04 system. In case of issues, check the [detailed installation instructions](INSTALL.md). 
-
-**Windows:** (NOT Recommended!) Check [these installation instructions](INSTALL_win.md). 
-
-#### Let's test it!
-Activate the conda environment and run the script pytracking/run_webcam.py to run ATOM using the webcam input.  
-```bash
-conda activate pytracking
-cd pytracking
-python run_webcam.py dimp dimp50    
-```  
-
-
-
-## Main Contributors
-
-* [Martin Danelljan](https://martin-danelljan.github.io/)  
-* [Goutam Bhat](https://www.vision.ee.ethz.ch/en/members/detail/407/)