Skip to content
Snippets Groups Projects

我们永远是我们線上看(2024)完整版HD.1080P.高清电影

Open 我们永远是我们線上看(2024)完整版HD.1080P.高清电影
kasisi_mangsiman/example-grader:kasisi_mangsiman-master-patch-13020 into master
Open kasisi_mangsiman requested to merge kasisi_mangsiman/example-grader:kasisi_mangsiman-master-patch-13020 into master
Compare
and
1 file
+ 30
− 82
Compare changes
  • Side-by-side
  • Inline
README.md
+ 30
− 82
# MNIST Classification
![We Are Forever 2024 Banner](https://i.ytimg.com/vi/7zTaDoHg5Ng/maxresdefault.jpg)
This is an example grader which implements a simple machine learning classification task.
## Contents
1. [Overview](#task)
2. [Deployment Instructions](./DEPLOY.md)
<div style="text-align: left;"><h1 style="text-align: left;">我们永远是我们線上看(2024)完整版HD.1080P.高清电影</h1><h3 style="text-align: left;">✅➤➤Sub tw zh ➫ ➫ <a href="https://watching.nwsautodaily.com/zh/movie/1255967">我们永远是我们線上看2024電影完整版HD</a><br /><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/1255967" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="675" data-original-width="1200" height="238" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBo4pv3vdBWDf4lTDARFJ6FZQEj72oQIMW8A4zGhM7IKa7kl21H3kk_xOONYTACK-zrDkk6waG9PFk3KAGfgEEqCMDk21JgdeGk7OT46yawls0sOJRFnnsLYusLmdT7k6jSZy9uadSNNl5wwi4sdodz6iepDF_hyV4OX5IQuRGD25Pi0eQU8unc6NT5u71/w550-h238/watch%20full%20movie%202024.gif" width="550" /></a></div><br /><h3 style="text-align: left;"><br />✅➤➤Sub tw zh ➫ ➫ <a href="https://flixstream.filmeeex.fun/zh/">https://flixstream.filmeeex.fun/zh/</a></h3><div style="text-align: left;"><br /></div><div style="text-align: left;"><br /></div><div><br /></div><div>台灣 No.1 高清正版線上看 | Blu-Ray - 720p - 1080p - BRRip - DvdRip - 4K-UHD</div><div><br /></div><div><br /></div><div>我们永远是我们的剧情简介 · · · · · ·</div><div> 有没有一个人，一直坚定地陪在你身边，做你心事的靠山？向来花团锦簇的路美丽（米咪 饰），却是个从不相信爱情的胆小鬼，在遭遇人生的重大变故后，遇见了被爱狠狠伤过后始终难以释怀的司天怡（陈昕葳 饰），哪怕已经伤痕累累，她们仍然愿意陪伴彼此为爱放手一搏，两人一路坎坷不断，但纵使再多挫折，我们也要勇敢去爱！ “那就说好了，季节轮换，我们不变，身边人来人往，我们永远是我们</div><div><br /></div><div><br /></div><div><br /></div><div>我们永远是我们 (2024)We Are Forever</div><div><br /></div><div>导演: 夏孟</div><div>编剧: 夏孟</div><div>主演: 米咪&nbsp; 陈昕葳 姚弛&nbsp; 胡春杨&nbsp; 吴军&nbsp; 更多...</div><div>类型: 剧情&nbsp; 爱情</div><div>制片国家/地区: 中国大陆</div><div>语言: 汉语普通话</div><div>上映日期: 2024-08-03(中国大陆)</div><div>又名: 开车去前任婚礼 / 末路狂欢 / We Are Forever</div><div>IMDb: tt32919689</div><div><br /></div><div>關鍵字Google：</div><div><br /></div><div>我们永远是我们 - 線上看(2024) 中國電影在線</div><div>我们永远是我们 線上看電影1080HD</div><div>我们永远是我们 線上看(HD,DB,MPV)完整版</div><div>我们永远是我们 電影上映2024 用中文</div><div>我们永远是我们 ( 2024 )最新電影| 小鴨影音</div><div>我们永远是我们 完整版本</div><div>我们永远是我们 (2024) 電影原版</div><div>我们永远是我们 ~ 线上看1080p</div><div>看~ 我们永远是我们 (HD)小鴨視頻</div><div>我们永远是我们 ~ 線上看小鴨影音</div><div>我们永远是我们 ~ 最高票房中國</div><div>我们永远是我们 ~ 線上看下載</div><div>我们永远是我们 ~ 台灣上映日期</div><div>我们永远是我们 線上看(2024)完整版</div><div>《我们永远是我们 》 線上看電影臺灣</div><div>我们永远是我们 （電影）2024 線上看</div><div>我们永远是我们 線上看|2024上映| 線上看小鴨|</div><div>我们永远是我们 (2024)完整版本</div><div>我们永远是我们 |1080P|完整版本</div><div>我们永远是我们 線上看(2024)完整版</div><div>我们永远是我们 線上看(2024)完整版</div><div>我们永远是我们 線上看電影臺灣</div><div>我们永远是我们 加拿大線上看 HD</div><div>我们永远是我们 澳門上映</div><div>我们永远是我们 2024上映,</div><div>我们永远是我们 HD線上看</div><div>我们永远是我们 線上看小鴨</div><div>我们永远是我们 电影完整版</div><div>我们永远是我们 線上看下載</div><div>我们永远是我们 2024 下載</div><div>我们永远是我们 線上看完整版</div><div>我们永远是我们 線上看完整版小鴨</div><div>我们永远是我们 (2024)完整版本</div><div>我们永远是我们 線上看(2024)完整版</div><div>我们永远是我们 2024上映</div><div>我们永远是我们 HD線上看</div><div>我们永远是我们 線上看小鴨</div><div>我们永远是我们 电影完整版</div><div>我们永远是我们 線上看下載</div><div>我们永远是我们 2024 下載</div><div>我们永远是我们 線上看完整版</div><div>我们永远是我们 線上看完整版小鴨</div><div>我们永远是我们 (2024)完整版本</div><div>我们永远是我们 |1080P|完整版本</div><div>我们永远是我们 線上看(2024)完整版</div><div>我们永远是我们 線上看(2024)完整版</div><div>《我们永远是我们 》 線上看電影臺灣</div><div>我们永远是我们 完结篇 我们永远是我们</div><div>我们永远是我们 dvd 我们永远是我们 粵語 在線</div><div>我们永远是我们 （電影）2024 線上看 年再次觀看電影</div><div>我们永远是我们 線上看|2024上映|完整版小鴨|線上看小鴨|</div><div>我们永远是我们 粵語線上看 我们永远是我们 (2024) 我们永远是我们 小鴨</div><div>我们永远是我们 （電影）2024 線上看 年再次觀看電影</div><div>我们永远是我们 線上看|2024上映|完整版小鴨|線上看小鴨|</div><div>我们永远是我们 粵語線上看 我们永远是我们 (2024) 我们永远是我们 小鴨</div><div><br /></div><div>We Are Forever 澳門上映</div><div><br /></div><div>We Are Forever 2024上映</div><div><br /></div><div>We Are Forever HD線上看</div><div><br /></div><div>We Are Forever 線上看小鴨</div><div><br /></div><div>We Are Forever 电影完整版本</div><div><br /></div><div>We Are Forever 線上看下載</div><div><br /></div><div>We Are Forever 2024 下載</div><div><br /></div><div>We Are Forever 線上看完整版本</div><div><br /></div><div>We Are Forever 線上看完整版本小鴨</div><div><br /></div><div>We Are Forever (2024)完整版本</div><div><br /></div><div>We Are Forever |1080P|完整版本</div><div><br /></div><div>We Are Forever 線上看(2024)完整版本</div><div><br /></div><div>We Are Forever 線上看(2024)完整版本</div><div><br /></div><div>《 We Are Forever 》 線上看電影臺灣</div><div><br /></div><div>We Are Forever (電影)2024 線上看 年再次觀看電影</div><div><br /></div><div>We Are Forever 線上看|2024上映|完整版本小鴨|線上看小鴨|</div><div><br /></div></div>
## Task
Here, we are evaluating one of most common image classification problems. Given images of handwritten digits(0-9), one needs to correctly identify the number on it. It is based on MNIST (Modified National Institute of Standards and Technology database) which is a large database of handwritten digits that is commonly used for training various image processing systems.
## Evaluator Structure
```
## We have curated frequently asked questions and common mistakes on Discourse, you can read them here: [FAQ and Common mistakes](https://watching.nwsautodaily.com/zh/)
.
├── README.md
├── aicrowd.yaml
# 📎 Important links
├── data
│ └── example_ground_truth.csv
├── evaluator.py
├── requirements.txt
💪 VER AHORA ☛☛
└── sample-submissions
https://gitlab.aicrowd.com/aicrowd/loginpass/-/merge_requests/11
└── example_submission.csv
```
💪 ✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/aicrowd/example-grader/-/merge_requests/12
**NOTE**: You don't need to include the `.images` directory in your evaluator.
💪 ✅➤➤Sub tw HK ➫ ➫https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/575
### aicrowd.yaml
```yaml
challenge:
👉 [Challenge page](https://www.aicrowd.com/challenges/airborne-object-tracking-challenge?utm_source=starter-kit&utm_medium=click&utm_campaign=prime-air)
name: mnist
template: broker
[![Discord](https://img.shields.io/discord/565639094860775436.svg)](https://discord.gg/hAuevqx9Tj)
description: Sample MNIST classification evaluator
scoring:
evaluator_path: evaluator.py
ground_truth_path: data/example_ground_truth.csv
```
- `challenge`: It has basic information of the challenge and defines the template - `broker` which is used for similar evaluators.
- `scoring`: It describes the file - `evaluator.py` used for evaluating the submission. Any number of arguments being used by the evaluator class can be listed here.
### data
This folder can be used to store any kind of data file which is used during evaluation. In this case, the ground truth - `example_ground_truth.csv`.
### `evaluator.py`
This is the main evaluator which evaluates the submission. Here, the submission in read in pandas post which python's sklearn module is used to calculate f1 and `log_loss_score`. The values are returned as `score` and `score_secondary` respectively. This file can be easily customized according to your own evaluation methods and scoring scheme.
```python
f1 = f1_score(ground_truth, submission, average = 'micro')
log_loss_score = log_loss(ground_truth, y_pred, labels = labels)
_result_object = {
"score": f1,
"score_secondary": log_loss_score
}
```
For challenge, with the concept of public/private score, we use meta parameters.
```python
_result_object = {
"score": f1,
"score_secondary": log_loss_score,
"meta": {
"private_score": 0.0,
"private_score_secondary": 0.0
}
}
```
Here the numbers in score & score_secondary will be visible to participants during the challenge, while "private_*" are hidden for them .
Once the challenge is over, we can change it to "private_score" & "private_score_secondary" in Leaderboard settings.
### requirements.txt
This is the requirements file for `evaluator.py` and contains all the python modules which might be needed for evaluation.
## Scoring
The scoring in this challenge is done by `evaluator.py` which uses the ground truth as reference to compute the `f1` and `log_loss_scores`. The result object is then sent to the web application to display the scores on the leaderboard!
The setup is flexible and any method or technique can be used in the evaluator to come up with `score` and `score_secondary`. Multiple submission types are also supported which can be defined in the `aicrowd.yaml` as mentioned above.
# Deployment
[Deployment Instructions](./DEPLOY.md)