diff --git a/examples/agp-pruning/resnet50.schedule_agp.filters.yaml b/examples/agp-pruning/resnet50.schedule_agp.filters.yaml
index 04e35006a01422904783557d96b02de06a95ee39..0289337ff468c88d0f9041cfd9284d6a9a4e77c4 100755
--- a/examples/agp-pruning/resnet50.schedule_agp.filters.yaml
+++ b/examples/agp-pruning/resnet50.schedule_agp.filters.yaml
@@ -5,7 +5,7 @@
 # No. of Parameters: 12,335,296 (of 25,502,912) = 43.37% dense (56.63% sparse)
 # Total MACs: 1,822,031,872 (of 4,089,184,256) = 44.56% compute = 2.24x
 #
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-split=0   --num-best-scores=10 --name="resnet50_filters_v3.2"
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=../agp-pruning/resnet50.schedule_agp.filters.yaml --validation-split=0   --num-best-scores=10 --name="resnet50_filters_v3.2"
 #
 # Parameters:
 # +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+