From 1446bfc2fbd6516cb9b079d41fc86e4ad6086ace Mon Sep 17 00:00:00 2001
From: Mu Li <muli@cs.cmu.edu>
Date: Mon, 19 Dec 2016 17:17:18 -0800
Subject: [PATCH] [image-classification] default use min_random_scale=1, add
 data/imagenet1k-val.sh (#4295)

---
 .../data/imagenet1k-val.sh                    | 22 +++++++++++++++++++
 .../image-classification/train_imagenet.py    |  3 ++-
 2 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100755 example/image-classification/data/imagenet1k-val.sh

diff --git a/example/image-classification/data/imagenet1k-val.sh b/example/image-classification/data/imagenet1k-val.sh
new file mode 100755
index 000000000000..51f8130103fd
--- /dev/null
+++ b/example/image-classification/data/imagenet1k-val.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+# This file download the imagnet-1k validation dataset and convert it into a rec
+# file. One need to provide the URL for the ILSVRC2012_img_val.tar, which can be
+# find at http://www.image-net.org/download-images
+#
+# Example usage (replace the URL with the correct one):
+# ./imagenet1k-val.sh http://xxxxxx/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar
+
+if [ ! -e ILSVRC2012_img_val.tar ]; then
+    wget $1
+fi
+mkdir -p val
+tar -xf ILSVRC2012_img_val.tar -C val
+wget http://data.mxnet.io/models/imagenet/resnet/val.lst -O imagenet1k-val.lst
+
+CUR_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+MX_DIR=${CUR_DIR}/../../../
+
+python ${CUR_DIR}/../../../tools/im2rec.py --resize 256 --quality 90 --num-thread 16 imagenet1k-val val/
+
+rm -rf val
diff --git a/example/image-classification/train_imagenet.py b/example/image-classification/train_imagenet.py
index 432393e57044..db190275a18b 100644
--- a/example/image-classification/train_imagenet.py
+++ b/example/image-classification/train_imagenet.py
@@ -23,7 +23,8 @@
         num_classes      = 1000,
         num_examples     = 1281167,
         image_shape      = '3,224,224',
-        min_random_scale = 0.533, # assume input image has min size 480, 0.533 = 256/480
+        min_random_scale = 1, # if input image has min size k, suggest to use
+                              # 256.0/x, e.g. 0.533 for 480
         # train
         num_epochs       = 80,
         lr_step_epochs   = '30,60',