Fix a bug; Fix some typos and update the model link in README

README.md

@@ -21,29 +21,29 @@ Our current implementation only supports GPU so you need a GPU and need to have
 ### Compiling Corner Pooling Layers
 You need to compile the C++ implementation of corner pooling layers. 
 ```
-cd <CornetNet dir>/models/py_utils/_cpools/
+cd <CornerNet dir>/models/py_utils/_cpools/
 python setup.py install --user
 ```
 
 ### Compiling NMS
 You also need to compile the NMS code (originally from [Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/cpu_nms.pyx) and [Soft-NMS](https://github.com/bharatsingh430/soft-nms/blob/master/lib/nms/cpu_nms.pyx)).
 ```
-cd <CornetNet dir>/external
+cd <CornerNet dir>/external
 make
 ```
 
 ### Installing MS COCO APIs
 You also need to install the MS COCO APIs.
 ```
-cd <CornetNet dir>/data
+cd <CornerNet dir>/data
 git clone git@github.com:cocodataset/cocoapi.git coco
-cd <CornetNet dir>/data/coco/PythonAPI
+cd <CornerNet dir>/data/coco/PythonAPI
 make
 ```
 
 ### Downloading MS COCO Data
-- Download the training/validation split we use in our paper from [here](https://drive.google.com/file/d/1dop4188xo5lXDkGtOZUzy2SHOD_COXz4/view?usp=sharing) (originally from [Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn/tree/master/data))
-- Unzip the file and place `annotations` under `<CornetNet dir>/data/coco`
+- Download the training/validation split we use in our paper from [here](https://drive.google.com/open?id=16bbMAyykdZr2_7afiMZrvvn4xkYa-LYk) (originally from [Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn/tree/master/data))
+- Unzip the file and place `annotations` under `<CornerNet dir>/data/coco`
 - Download the images (2014 Train, 2014 Val, 2017 Test) from [here](http://cocodataset.org/#download)
 - Create 3 directories, `trainval2014`, `minival2014` and `testdev2017`, under `<CornerNet dir>/data/coco/images/`
 - Copy the training/validation/testing images to the corresponding directories according to the annotation files

config/CornerNet.json

@@ -35,6 +35,7 @@
 
         "border": 128,
         "gaussian_bump": true,
+        "gaussian_iou": 0.3,
 
         "input_size": [511, 511],
         "output_sizes": [[128, 128]],

sample/utils.py

@@ -31,19 +31,19 @@ def gaussian_radius(det_size, min_overlap):
     b1  = (height + width)
     c1  = width * height * (1 - min_overlap) / (1 + min_overlap)
     sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1)
-    r1  = (b1 + sq1) / 2
+    r1  = (b1 - sq1) / (2 * a1)
 
     a2  = 4
     b2  = 2 * (height + width)
     c2  = (1 - min_overlap) * width * height
     sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2)
-    r2  = (b2 + sq2) / 2
+    r2  = (b2 - sq2) / (2 * a2)
 
     a3  = 4 * min_overlap
     b3  = -2 * min_overlap * (height + width)
     c3  = (min_overlap - 1) * width * height
     sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3)
-    r3  = (b3 + sq3) / 2
+    r3  = (b3 + sq3) / (2 * a3)
     return min(r1, r2, r3)
 
 def _get_border(border, size):