Add Features and Organization comparision

README.md

@@ -13,9 +13,17 @@ EvalAI is an open source web application that helps researchers, students and da
 
 In recent years, it has become increasingly difficult to compare an algorithm solving a given task with other existing approaches. These comparisons suffer from minor differences in algorithm implementation, use of non-standard dataset splits and different evaluation metrics. By providing a central leaderboard and submission interface, we make it easier for researchers to reproduce the results mentioned in the paper and perform reliable & accurate quantitative analysis. By providing swift and robust backends based on map-reduce frameworks that speed up evaluation on the fly, EvalAI aims to make it easier for researchers to reproduce results from technical papers and perform reliable and accurate analyses.
 
-<p align="center"><img width="65%" src="src/assets/images/kaggle_comparison.png" /></p>
-
-A question we’re often asked is: Doesn’t Kaggle already do this? The central differences are:
+|          Features          |          OpenML          |         TopCoder         |          Kaggle          |         AICrowd          |          ParlAI          |         Codalab          |       EvalAI       |
+| :------------------------: | :----------------------: | :----------------------: | :----------------------: | :----------------------: | :----------------------: | :----------------------: | :----------------: |
+|    AI challenge hosting    | :heavy_multiplication_x: |    :white_check_mark:    |    :white_check_mark:    |    :white_check_mark:    | :heavy_multiplication_x: |    :white_check_mark:    | :white_check_mark: |
+|       Custom metrics       | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: |    :white_check_mark:    |    :white_check_mark:    |    :white_check_mark:    | :white_check_mark: |
+|   Multiple phases/splits   | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: |    :white_check_mark:    | :heavy_multiplication_x: |    :white_check_mark:    | :white_check_mark: |
+|        Open source         |    :white_check_mark:    | :heavy_multiplication_x: | :heavy_multiplication_x: |    :white_check_mark:    |    :white_check_mark:    |    :white_check_mark:    | :white_check_mark: |
+|     Remote evaluation      | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: |    :white_check_mark:    |    :white_check_mark:    | :white_check_mark: |
+|      Human evaluation      | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: |    :white_check_mark:    | :heavy_multiplication_x: | :white_check_mark: |
+| Evaluation in Environments | :heavy_multiplication_x: | :heavy_multiplication_x: | :heavy_multiplication_x: |    :white_check_mark:    | :heavy_multiplication_x: | :heavy_multiplication_x: | :white_check_mark: |
+
+How is EvalAI different from others? The central differences are:
 
 - **Custom Evaluation Protocols and Phases**: We have designed versatile backend framework that can support user-defined evaluation metrics, various evaluation phases, private and public leaderboard.
 
@@ -31,6 +39,13 @@ A question we’re often asked is: Doesn’t Kaggle already do this? The central
 
 Our ultimate goal is to build a centralized platform to host, participate and collaborate in AI challenges organized around the globe and we hope to help in benchmarking progress in AI.
 
+## Features
+
+- **Results compiled natively which decreases network dependence**
+- **Easy to export project to Android and IOS with a single codebase**
+- **Test cases help identify incorrect settings during development**
+- **Docker builds the project automatically - No hustle on workers and services !**
+
 ## Performance comparison
 
 Some background: Last year, the [Visual Question Answering Challenge (VQA) 2016](http://www.visualqa.org/vqa_v1_challenge.html) was hosted on some other platform, and on average evaluation would take **~10 minutes**. EvalAI hosted this year's [VQA Challenge 2017](https://evalai.cloudcv.org/featured-challenges/1/overview). This year, the dataset for the [VQA Challenge 2017](http://www.visualqa.org/challenge.html) is twice as large. Despite this, we’ve found that our parallelized backend only takes **~130 seconds** to evaluate on the whole test set VQA 2.0 dataset.