Skip to content
Snippets Groups Projects

神偷奶爸4線上看(2024)完整版本HD-TAIWAN

1 file
+ 13
151
Compare changes
  • Side-by-side
  • Inline
+ 13
151
![Airborne Banner](https://i.imgur.com/MxW7ySd.jpg)
![Vice-Versa 2 Banner](https://i.ytimg.com/vi/_-Q5WIEwUYQ/maxresdefault.jpg)
# 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)
<h1 style="text-align: left;">神偷奶爸4線上看(2024)完整版HD.1080P.高清电影</h1><div><br /></div><div>神偷奶爸4線上看(2024)完整版本HD-TAIWAN</div><div><br /></div><div><br /></div><div><h3 style="text-align: left;">✅➤➤Sub tw zh ➫ ➫ <a href="https://watching.nwsautodaily.com/zh/movie/519182/despicable-me-4">神偷奶爸4線上看2024電影完整版HD</a><br /><br />✅➤➤Sub English ➫ ➫ <a href="https://lawe.sensacinema.site/en">https://lawe.sensacinema.site/en</a></h3><div class="separator" style="clear: both; text-align: center;"><a href="https://watching.nwsautodaily.com/zh/movie/519182/despicable-me-4" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="675" data-original-width="1200" height="359" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBooWh05G4Snj-_CCznt6-gPrfZxX5ybkZBplYX5BZ7dFxrZLUaCVkysqbfBnVtx5CesNT_Zla6xUbhTnJmTQD7D5qWpXAJsoV8YFLpjBJ5VFL4QK2qfxp7Vh-60gacEGdyb7RpPmCxc2uJT5vg8yoc_hyqWUfBuY9O72F6EfaqIzcYVwvgUCX2n9qpF-6/w518-h359/watch%20full%20movie%202024.gif" width="518" /></a></div><br /><div style="text-align: left;"><br /></div><div><br /></div><div>𝟜𝕂 𝕌ℍ𝔻 | 𝟙𝟘𝟠𝟘ℙ 𝔽𝕌𝕃𝕃 ℍ𝔻 | 𝟟𝟚𝟘ℙ ℍ𝔻</div></div><div><br /></div><div><br /></div><div>看 神偷奶爸4 在線觀-1080 免費完整版HD 看 神偷奶爸4 - 線上看【2024】 完整版 看 神偷奶爸4 ▷ 線上看完整版- HD2024年电影 神偷奶爸4 (2024) 電影完整版 . 神偷奶爸4 完整版在線電影中文版 . 看電影 (神偷奶爸4 - Despicable Me 4) 免費在線觀看高清 1080P.</div><div><br /></div><div><br /></div><div><br /></div><div><br /></div><div>全世界最受歡迎的大壞蛋又變成反大壞蛋聯盟探員的格魯再度強勢回歸,在照明娛樂出品的《神偷奶爸4》一片中迎來一個精彩刺激、大膽冒險和小小兵再度把一切搞得天翻地覆的全新時代上映日期:2024/07/05</div><div><br /></div><div>导演: 克里斯·雷纳德 / 帕特里克·德拉吉</div><div>编剧: 麦克·怀特 / 肯·道里欧</div><div>主演: 史蒂夫·卡瑞尔 / 克里斯汀·韦格 / 威尔·法瑞尔 / 索菲娅·维加拉 / 皮埃尔·柯芬 / 更多...</div><div>类型: 喜剧 / 动画 / 冒险</div><div>制片国家/地区: 美国</div><div>语言: 英语</div><div>上映日期: 2024-07-12(中国大陆) / 2024-06-16(翠贝卡电影节) / 2024-07-03(美国)</div><div>网站: https://watching.nwsautodaily.com/</div><div>片长: 105分钟</div><div>又名: 卑鄙的我4</div><div>IMDb: tt7510222</div><div><br /></div><div>神偷奶爸4的剧情简介 · · · · · ·</div><div><br /></div><div> 格鲁(史蒂夫·卡瑞尔 Steve Carell 配音)和露西(克里斯汀·韦格 Kristen Wiig 配音)一家即将展开家庭生活的新篇章,迎接新成员“迷你格鲁”,而这个小宝宝也会用尽方法折磨格鲁这个新手老爸。</div><div> 新反派恶霸麦斯(威尔·法瑞尔 Will Ferrell 配音)和他既致命危险又美丽性感的女友瓦伦蒂娜(索菲娅·维加拉 Sofía Vergara 配音)来势汹汹,格鲁一家面临未知危险。此时,全新超级小黄人重磅亮相,成为帮助格鲁一家度过这次危机的“秘密武器”。一场惊险刺激、笑料百出的全新冒险即将展开</div><div><br /></div><div>神偷奶爸4 - 線上看(2024) 中國電影在線</div><div><br /></div><div>神偷奶爸4 線上看電影1080HD</div><div><br /></div><div>神偷奶爸4 線上看(HD,DB,MPV)完整版</div><div><br /></div><div>神偷奶爸4 電影上映2024 用中文</div><div><br /></div><div>神偷奶爸4 ( 2024 )最新電影| 小鴨影音</div><div><br /></div><div>神偷奶爸4 完整版本</div><div><br /></div><div>神偷奶爸4 (2024) 電影原版</div><div><br /></div><div>神偷奶爸4 ~ 线上看1080p</div><div><br /></div><div>看~ 神偷奶爸4 (HD)小鴨視頻</div><div><br /></div><div>神偷奶爸4 ~ 線上看小鴨影音</div><div><br /></div><div>神偷奶爸4 ~ 最高票房中國</div><div><br /></div><div>神偷奶爸4 ~ 線上看下載</div><div><br /></div><div>神偷奶爸4 ~ 台灣上映日期</div><div><br /></div><div>神偷奶爸4 線上看(2024)完整版</div><div><br /></div><div>《神偷奶爸4 》 線上看電影臺灣</div><div><br /></div><div>神偷奶爸4 (電影)2024 線上看</div><div><br /></div><div>神偷奶爸4 線上看|2024上映| 線上看小鴨|</div><div><br /></div><div>神偷奶爸4 (2024)完整版本</div><div><br /></div><div>神偷奶爸4 |1080P|完整版本</div><div><br /></div><div>神偷奶爸4 線上看(2024)完整版</div><div><br /></div><div>神偷奶爸4 線上看(2024)完整版</div><div><br /></div><div>神偷奶爸4 線上看電影臺灣</div><div><br /></div><div>神偷奶爸4 加拿大線上看 HD</div><div><br /></div><div>神偷奶爸4 澳門上映</div><div><br /></div><div>神偷奶爸4 2024上映,</div><div><br /></div><div>神偷奶爸4 HD線上看</div><div><br /></div><div>神偷奶爸4 線上看小鴨</div><div><br /></div><div>神偷奶爸4 电影完整版</div><div><br /></div><div>神偷奶爸4 線上看下載</div><div><br /></div><div>神偷奶爸4 2024 下載</div><div><br /></div><div>神偷奶爸4 線上看完整版</div><div><br /></div><div>神偷奶爸4 線上看完整版小鴨</div><div><br /></div><div>神偷奶爸4 (2024)完整版本</div><div><br /></div><div>神偷奶爸4 線上看(2024)完整版</div><div><br /></div><div>神偷奶爸4 2024上映</div><div><br /></div><div>神偷奶爸4 HD線上看</div><div><br /></div><div>神偷奶爸4 線上看小鴨</div><div><br /></div><div>神偷奶爸4 电影完整版</div><div><br /></div><div>神偷奶爸4 線上看下載</div><div><br /></div><div>神偷奶爸4 2024 下載</div><div><br /></div><div>神偷奶爸4 線上看完整版</div><div><br /></div><div>神偷奶爸4 線上看完整版小鴨</div><div><br /></div><div>神偷奶爸4 (2024)完整版本</div><div><br /></div><div>神偷奶爸4 |1080P|完整版本</div><div><br /></div><div>神偷奶爸4 線上看(2024)完整版</div><div><br /></div><div>神偷奶爸4 線上看(2024)完整版</div><div><br /></div><div>《神偷奶爸4 》 線上看電影臺灣</div><div><br /></div><div>神偷奶爸4 完结篇 神偷奶爸4</div><div><br /></div><div>神偷奶爸4 dvd 神偷奶爸4 粵語 在線</div><div><br /></div><div>神偷奶爸4 (電影)2024 線上看 年再次觀看電影</div><div><br /></div><div>神偷奶爸4 線上看|2024上映|完整版小鴨|線上看小鴨|</div><div><br /></div><div>神偷奶爸4 粵語線上看 神偷奶爸4 (2024) 神偷奶爸4 小鴨</div><div><br /></div><div>神偷奶爸4 (電影)2024 線上看 年再次觀看電影</div><div><br /></div><div>神偷奶爸4 線上看|2024上映|完整版小鴨|線上看小鴨|</div><div><br /></div><div>神偷奶爸4 粵語線上看 神偷奶爸4 (2024) 神偷奶爸4 小鴨</div>
[![Discord](https://img.shields.io/discord/565639094860775436.svg)](https://discord.gg/hAuevqx9Tj)
# 📎 Important links
This repository is the main Airborne Object Tracking challenge **Submission template and Starter kit**!
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
# 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)
<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>
# 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**
![](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
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
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**
```
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.
5. Try out random prediction codebase present in `test.py`.
## 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`.
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:
```
.
├── 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:
```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.
## How to make submission
💪 VER AHORA ☛☛ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/70
👉 [SUBMISSION.md](/docs/SUBMISSION.md)
**Best of Luck** :tada: :tada:
💪 REGARDER ☛☛ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/37
# 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.
💪 ✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/89
> :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
💪 ✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/90
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)
```
#> cp siam-mot/Dockerfile Dockerfile
```
✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/92
# Other Concepts
✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/93
## Time constraints
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.
✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/93
## Local evaluation
✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/94
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).
✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/95
## Hardware used for evaluations
✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/96
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)*
## 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
🗣️ &nbsp;Discussion Forum: https://www.aicrowd.com/challenges/airborne-object-tracking-challenge/discussion
🏆 &nbsp;Leaderboard: https://www.aicrowd.com/challenges/airborne-object-tracking-challenge/leaderboards
Loading