Add logo and resources folder (open-mmlab#3230)

* add logo and resources folder

* update logo size

* change logo size to 400

* change logo size to 600

Author: hellock

README.md

@@ -1,18 +1,20 @@
-# MMDetection
+<div align="center">
+  <img src="resources/mmdet-logo.png" width="600"/>
+</div>
 
 **News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155).
 
 Documentation: https://mmdetection.readthedocs.io/
 
 ## Introduction
 
-The master branch works with **PyTorch 1.3 to 1.5**.
-The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage.
-
 MMDetection is an open source object detection toolbox based on PyTorch. It is
 a part of the OpenMMLab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).
 
-![demo image](demo/coco_test_12510.jpg)
+The master branch works with **PyTorch 1.3 to 1.5**.
+The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage.
+
+![demo image](resources/coco_test_12510.jpg)
 
 ### Major features
 

docs/getting_started.md

@@ -344,7 +344,7 @@ We provide lots of useful tools under `tools/` directory.
 
 You can plot loss/mAP curves given a training log file. Run `pip install seaborn` first to install the dependency.
 
-![loss curve image](../demo/loss_curve.png)
+![loss curve image](../resources/loss_curve.png)
 
 ```shell
 python tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]

docs/robustness_benchmarking.md

@@ -18,7 +18,7 @@ This page provides basic tutorials how to use the benchmark.
 }
 ```
 
-![image corruption example](../demo/corruptions_sev_3.png)
+![image corruption example](../resources/corruptions_sev_3.png)
 
 ## About the benchmark
 

docs/tutorials/data_pipeline.md

@@ -15,7 +15,7 @@ defines how to process the annotations and a data pipeline defines all the steps
 A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.
 
 We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
-![pipeline figure](../../demo/data_pipeline.png)
+![pipeline figure](../../resources/data_pipeline.png)
 
 The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.