Update README.md

README.md

@@ -11,7 +11,64 @@ This is an evolving dataset, where we will release more data as the dataset grow
 ![image1](https://i.imgur.com/zS2Nbf0.png)
 
 # Baseline
-**TODO** : @Nikhil : Please add some description of this baseline, links to MMDection paper etc. 
+MMdetection is an open source object detection toolbox based on PyTorch, with a large Model Zoo with many customised models that can be plugged and tested in with just a single config file modification. PYou can read more about it at: [mmdetection github](https://github.com/open-mmlab/mmdetection/)
+
+Follow the installation instructions as given in the above link.
+# Training with MMDetection:
+Let us look at training MMDetection using Hybrid Task Cascade [HTC research paper](https://arxiv.org/abs/1901.07518).
+
+A score of AP_50 of 0.526	and AR_50 of 0.729, can be achieved with Hybrid Task Cascade of Resnet50 Backbone.
+
+MMDetection provides us with a config file especially for HTC, available at [HTC config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc)
+
+Also make sure you have downloaded the training data to a subfolder of your project.
+
+Modify your config file and point your dataset variables to your data folder.
+
+As given in [MMDetection Getting Started](https://github.com/open-mmlab/mmdetection/blob/master/docs/GETTING_STARTED.md),
+You can use:
+
+    python tools/train.py ${CONFIG_FILE}
+to train the model on a single GPU or
+
+    ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
+to train the model on multiple GPUs.
+
+Make sure you have edited the config file to point to the dataset and also have made changes to the number of classes.
+If you are going to use the dataloader from the mmdetection.
+
+## Testing with MMDetection:
+To test your results with MMDetection, 
+you can use the commands:
+```
+*single-gpu testing*
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
+
+*multi-gpu testing*
+./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
+```
+
+**Log Analysis**
+
+The training logs can be analyzed using the plot_curve provided with the mmdetection:
+
+```
+import os
+import matplotlib
+%matplotlib inline
+from tools.analyze_logs import plot_curve
+matplotlib.rcParams['figure.figsize'] = [20, 10]
+args = {
+    'keys':['segm_mAP_50'],
+    'legend':'segm_mAP_50',
+    'backend': None,
+    'json_logs': [os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json'],
+    'title': 'loss'
+}
+print(os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json')
+plot_curve([os.getcwd()+'/work_dirs/htc_r50_fpn/20191206_105437.log.json'], args)
+```
+
 
 # Installation