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![Airborne Banner](https://i.imgur.com/MxW7ySd.jpg)
<div style="text-align: left;"><h1 style="text-align: left;">[.VOIR.] FILM!*— Vice-versa 2 en Streaming-VF VOSTFR en Français et Complet</h1><div><br /></div><h4 style="text-align: left;">Regarder ➤➤ <a href="https://watching.nwsautodaily.com/fr/movie/1022789">Vice-versa 2 Streaming VF ou VOSTFR 100%</a></h4><h4 style="text-align: left;"><br />Regarder ➤➤ <a href="https://watching.nwsautodaily.com/fr/movie/1022789">Vice-versa 2 Streaming VF ou VOSTFR 100%</a></h4><div><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://flixstream.filmeeex.fun/fr/movie/1022789" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="675" data-original-width="1200" height="355" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjY2dnSGL29iOp9cblAQUVj54F1PhO9FFXjPhoyqa2N9DqM0QBuQuR2ET0HVtV1AjxWHzxvlzmHpc1bvaY14JECY9dwJEq5xFF1FZCnrpLJiXpdwx4PZGWqEfaguYjTHhCaOp2empm_r1WKn9DHHuRQkJyACsmoSpm_y8Eg1ygK1E2gnNOUIKJqo_NownJl/w548-h355/watch%20full%20movie%202024.gif" width="548" /></a></div><br /><div><br /></div><div><br /></div><div><br /></div></div>
# Airborne Object Tracking Challenge Starter Kit
👉 [Challenge page](https://www.aicrowd.com/challenges/airborne-object-tracking-challenge?utm_source=starter-kit&utm_medium=click&utm_campaign=prime-air)
[![Discord](https://img.shields.io/discord/565639094860775436.svg)](https://discord.gg/hAuevqx9Tj)
This repository is the main Airborne Object Tracking challenge **Submission template and Starter kit**!
Il y a 17 secondes — Vice-versa 2 Streaming VF ou VOSTFR 100% gratuit, voir le film complet en français et en bonne qualité.
Clone the repository to compete now!
**This repository contains**:
* **Documentation** on how to submit your agent to the leaderboard
* **The procedure** for best practices and information on how we evaluate your agent, etc.
* **Starter code** for you to get started!
* **SiamMOT**: Siamese Multi-Object Tracking baseline
Vice-versa 2 est un film américano-britannique réalisé par Sam Taylor-Johnson et dont la sortie est prévue en 2024. Il s'agit d'un film biographique sur l'autrice-compositrice-interprète britannique Amy Winehouse et met en vedette Marisa Abela dans le rôle principal. Jack O'Connell, Eddie Marsan, Juliet Cowan et Lesley Manville figurent aussi au casting.
# Table of Contents
1. [Competition Procedure](#competition-procedure)
2. [How to access and use dataset](#how-to-access-and-use-dataset)
3. [How to start participating](#how-to-start-participating)
4. [How do I specify my software runtime / dependencies?](#how-do-i-specify-my-software-runtime-dependencies-)
5. [What should my code structure be like ?](#what-should-my-code-structure-be-like-)
6. [How to make submission](#how-to-make-submission)
7. [:star: SiamMOT baseline](#submit-siammot-baseline)
8. [Other concepts and FAQs](#other-concepts)
9. [Important links](#-important-links)
Vice-versa 2 retrace la vie et la musique d'Amy Winehouse, à travers la création de l'un des albums les plus iconiques de notre temps, inspiré par son histoire d’amour passionnée et tourmentée avec Blake Fielder-Civil.
<p style="text-align:center"><img style="text-align:center" src="https://images.aicrowd.com/uploads/ckeditor/pictures/400/493d98aa-b7e5-45f8-aed1-640e4768f647_video.gif" width="1024"></p>
Reste à savoir si le public aura envie de se (re)plonger dans la discographie d’Amy Winehouse le 24 décembre, lors de la sortie du film.
# Competition Procedure
The main task of the competition is to detect a collision threat reliably. In this challenge, you will train your agents locally and then upload them to AIcrowd (via git) to be evaluated.
**The following is a high level description of how this round works**
Voici quelques critiques positives du film :
![](https://i.imgur.com/xzQkwKV.jpg)
1. **Sign up** to join the competition [on the AIcrowd website].(https://www.aicrowd.com/challenges/airborne-object-tracking-challenge)
2. **Clone** this repo and start developing your solution.
3. **Train** your models to detect objects and write inference code in `test.py`.
4. [**Submit**](#how-to-submit-a-model) your trained models to [AIcrowd Gitlab](https://gitlab.aicrowd.com) for evaluation [(full instructions below)](#how-to-submit-a-model). The automated evaluation setup will evaluate the submissions against the test dataset to compute and report the metrics on the leaderboard of the competition.
# How to access and use dataset
SensCritique : ""Un bon film d'horreur, qui respecte l'esprit de la série de jeux vidéo. L'atmosphère est tendue et les jumpscares sont efficaces.""
The starter kit contains dataset exploration notebooks and helper functions to access the dataset.
You can check the instructions for the same here: 👉 [DATASET.md](/docs/DATASET.md).
# How to start participating
## Setup
AlloCiné : ""Un film qui saura plaire aux fans de la série de jeux vidéo, mais aussi aux amateurs de films d'horreur en général.""
1. **Add your SSH key** to AIcrowd GitLab
You can add your SSH Keys to your GitLab account by going to your profile settings [here](https://gitlab.aicrowd.com/profile/keys). If you do not have SSH Keys, you will first need to [generate one](https://docs.gitlab.com/ee/ssh/README.html#generating-a-new-ssh-key-pair).
2. **Clone the repository**
YouTube : ""Un film d'horreur efficace, qui sait créer une atmosphère tendue et angoissante.""
```
git clone git@gitlab.aicrowd.com:amazon-prime-air/airborne-detection-starter-kit.git
```
3. **Install** competition specific dependencies!
```
cd airborne-detection-starter-kit
pip3 install -r requirements.txt
```
4. **Run local exploration notebook** present in `data/dataset-playground.ipynb` using `jupyter notebook` command locally.
Voici quelques critiques négatives du film :
5. Try out random prediction codebase present in `test.py`.
SensCritique : ""Un film d'horreur décevant, avec un scénario prévisible et des personnages peu développés.""
## How do I specify my software runtime / dependencies ?
We accept submissions with custom runtime, so you don't need to worry about which libraries or framework to pick from.
The configuration files typically include `requirements.txt` (pypi packages), `environment.yml` (conda environment), `apt.txt` (apt packages) or even your own `Dockerfile`.
AlloCiné : ""Un film qui manque d'originalité et qui ne parvient pas à renouveler le genre de l'horreur.""
You can check detailed information about the same in the 👉 [RUNTIME.md](/docs/RUNTIME.md) file.
## What should my code structure be like ?
Please follow the example structure as it is in the starter kit for the code structure.
The different files and directories have following meaning:
YouTube : ""Un film d'horreur médiocre, qui ne fait que reprendre les clichés du genre.""
```
.
├── aicrowd.json # Submission meta information - like your username
├── apt.txt # Packages to be installed inside docker image
├── data # Your local dataset copy - you don't need to upload it (read DATASET.md)
├── requirements.txt # Python packages to be installed
├── test.py # IMPORTANT: Your testing/inference phase code, must be derived from AirbornePredictor (example in test.py)
└── utility # The utility scripts to provide smoother experience to you.
├── docker_build.sh
├── docker_run.sh
├── environ.sh
└── verify_or_download_data.sh
```
Finally, **you must specify an AIcrowd submission JSON in `aicrowd.json` to be scored!**
The `aicrowd.json` of each submission should contain the following content:
Dans l'ensemble, les critiques du film Vice-versa 2 sont mitigées. Le film a été apprécié par certains critiques pour son atmosphère tendue et ses jumpscares efficaces, mais d'autres critiques l'ont trouvé décevant, notamment en raison de son scénario prévisible et de ses personnages peu développés.
```json
{
"challenge_id": "evaluations-api-airborne",
"grader_id": "evaluations-api-airborne",
"authors": ["aicrowd-bot"],
"tags": "change-me",
"description": "Random prediction model for Airborne challenge",
"gpu": false
}
```
This JSON is used to map your submission to the challenge - so please remember to use the correct `challenge_id` as specified above.
Please specify if your code will use a GPU or not for the evaluation of your model. If you specify `true` for the GPU, GPU will be provided and used for the evaluation.
Vous trouverez ici tous les films que vous pouvez diffuser en ligne, y compris les films projetés cette semaine. Si vous vous demandez quoi voir sur ce site Web, sachez qu'il couvre des genres tels que le crime, la science, la fiction, l'action, la romance, le thriller, la comédie, le drame et le film d'animation.
## How to make submission
👉 [SUBMISSION.md](/docs/SUBMISSION.md)
**Best of Luck** :tada: :tada:
Merci beaucoup. Nous informons tous ceux qui sont heureux de recevoir des nouvelles ou des informations sur le programme cinématographique de cette année et sur la façon de regarder vos films préférés. J'espère que nous pourrons être le meilleur partenaire pour vous permettre de trouver des recommandations pour vos films préférés. C'est tout de notre part, salutations !
# SiamMOT baseline
[SiamMOT](https://github.com/amazon-research/siam-mot) is a region-based Siamese Multi-Object Tracking network that detects and associates object instances simultaneously.
This repository contains [SiamMOT](https://github.com/amazon-research/siam-mot) baseline interface which you can submit and improve upon.
Merci d'avoir regardé la vidéo aujourd'hui.
> :warning: Submissions that make use of the the provided SIMA-MOT baseline will be considered for ranking only if use a different model (different weights) which improves EDR by at least 1.5% (that is EDR >= 0.685, AFDR >= 0.6415) and HFAR < 0.5/ FPPI< 0.0005 — improvement of 1.5% in EDR practically means 2 more encounters detected (out of 102) OR Keeps the same EDR = 0.6699 / AFDR = 0.6265 and reduces HFAR/ FPPI by at least 50% (e.g. HFAR <= 0.23, FPPI <= 0.0002)
## Additional Steps
1. Change your entrypoint i.e. `run.sh` from `python test.py` to `python siam_mot_test.py`.
2. Copy the Dockerfile present in `siam-mot/Dockerfile` to repository root.
3. Set `gpu: true` in your `aicrowd.yaml`.
4. Follow common steps shared in [SUBMISSION.md](/docs/SUBMISSION.md)
J'espère que vous aimez les vidéos que je partage. Donnez un coup de pouce, aimez ou partagez si vous aimez ce que nous avons partagé afin que nous soyons plus excités.
```
#> cp siam-mot/Dockerfile Dockerfile
```
# Other Concepts
## Time constraints
Éparpillez un sourire joyeux pour que le monde revienne dans une variété de couleurs.
You need to make sure that your model can predict airborne objects for each flight within 800 seconds, otherwise the submission will be marked as failed.
## Local evaluation
You can also test end to end evaluation on your own systems. The scripts are available in `core/metrics` folder.
A working example is also available as [Colab Notebook here](https://colab.research.google.com/drive/1hobQBEfIxdPtc0jeMBtQKce8flrCKBq1?usp=sharing).
## Hardware used for evaluations
We use p3.2xlarge to run your evaluations i.e. 8 vCPU, 61 GB RAM, V100 GPU.
*(please enable GPU by putting "gpu": true in your aicrowd.json file)*
1. **Sign up** to join the competition [watching.nwsautodaily.com].(https://watching.nwsautodaily.com/fr/movie/1022789)
2. **REGARDER** — Vice-Versa 2 en Streaming-VF [FR!] Gratuitement en Français
## Frequently Asked Questions
We have curated frequently asked questions and common mistakes on Discourse, you can read them here: [FAQ and Common mistakes](https://discourse.aicrowd.com/t/faqs-and-common-mistakes-while-making-a-submission/5781)
# 📎 Important links
💪 &nbsp;Challenge Page: https://www.aicrowd.com/challenges/airborne-object-tracking-challenge
💪 Telecharger: https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/6
🗣️ Telecharger: https://gitlab.aicrowd.com/chatgpt_espanol/perspectiva-de-davis-romero/-/merge_requests/2
🗣️ &nbsp;Discussion Forum: https://www.aicrowd.com/challenges/airborne-object-tracking-challenge/discussion
🏆 Telecharger: https://gitlab.aicrowd.com/zew/data-purchasing-challenge-2022-starter-kit/-/merge_requests/6
🏆 &nbsp;Leaderboard: https://www.aicrowd.com/challenges/airborne-object-tracking-challenge/leaderboards