From f67b3845ebfb072964fb23c7e47159d73055ae02 Mon Sep 17 00:00:00 2001 From: Sawrav Chowdhury <43489196+sawravchy@users.noreply.github.com> Date: Sat, 25 Jul 2020 02:43:04 +0600 Subject: [PATCH 1/3] Update README.md --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 061e0d4..85c140d 100644 --- a/README.md +++ b/README.md @@ -4,10 +4,9 @@ STAC is a simple yet effective SSL framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentation. - +

- -__This code is only used for research. This is not an official Google product.__ +

# Instruction @@ -205,6 +204,9 @@ tensorboard --logdir=${PRJROOT}/detection/train_log booktitle={arXiv:2005.04757} } ``` +## Disclaimer + +This code is only used for research. This is not an official Google product. # Acknowledgement From e3000e8ec7bd7ef44fa2f15d048e48dffffc9ea4 Mon Sep 17 00:00:00 2001 From: Sawrav Chowdhury <43489196+sawravchy@users.noreply.github.com> Date: Sat, 25 Jul 2020 02:58:30 +0600 Subject: [PATCH 2/3] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 85c140d..d42311f 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ STAC is a simple yet effective SSL framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentation. -

+ -

+ # Instruction From aa55294f41ba836199943016e4c2a5084bd8a1f3 Mon Sep 17 00:00:00 2001 From: Sawrav Chowdhury <43489196+sawravchy@users.noreply.github.com> Date: Sat, 25 Jul 2020 03:11:11 +0600 Subject: [PATCH 3/3] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d42311f..67b8525 100644 --- a/README.md +++ b/README.md @@ -64,17 +64,17 @@ wget http://models.tensorpack.com/FasterRCNN/ImageNet-R50-AlignPadding.npz There are three steps: - __1.__ Train a standard detector on labeled data -(`detection/scripts/coco/train_stg1.sh`). +[`detection/scripts/coco/train_stg1.sh`](./detection/scripts/coco/train_stg1.sh). - __2.__ Predict pseudo boxes and labels of unlabeled -data using the trained detector (`detection/scripts/coco/eval_stg1.sh`). +data using the trained detector [`detection/scripts/coco/eval_stg1.sh`](./detection/scripts/coco/eval_stg1.sh). - __3.__ Use labeled data and unlabeled data with pseudo labels to train a STAC detector -(`detection/scripts/coco/train_stg2.sh`). +[`detection/scripts/coco/train_stg2.sh`](./detection/scripts/coco/train_stg2.sh). -Besides instruction at here, __`detection/scripts/coco/train_stac.sh`__ +Besides instruction at here, [__`detection/scripts/coco/train_stac.sh`__](./detection/scripts/coco/train_stac.sh) provides a combined script to train STAC. -__`detection/scripts/voc/train_stac.sh`__ is a combined script to train STAC on PASCAL VOC. +[__`detection/scripts/voc/train_stac.sh`__](./detection/scripts/voc/train_stac.sh) is a combined script to train STAC on PASCAL VOC. The following example use labeled data as 10% train2017 and rest 90% train2017 data as unlabeled data.