From 178b5baa7cfed416e7b125cd4a597f134f992584 Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Tue, 11 Dec 2018 22:03:17 +0800
Subject: [PATCH] update README
---
README.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 8f217c7..83a49cc 100644
--- a/README.md
+++ b/README.md
@@ -194,7 +194,7 @@ Here is an example.
'bboxes': <np.ndarray> (n, 4),
'labels': <np.ndarray> (n, ),
'bboxes_ignore': <np.ndarray> (k, 4),
- 'labels_ignore': <np.ndarray> (k, 4) (optional field)
+ 'labels_ignore': <np.ndarray> (k, ) (optional field)
}
},
...
@@ -206,12 +206,12 @@ There are two ways to work with custom datasets.
- online conversion
You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods
- `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`, like [CocoDataset](mmdet/datasets/coco.py).
+ `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`, like [CocoDataset](mmdet/datasets/coco.py) and [VOCDataset](mmdet/datasets/voc.py).
- offline conversion
You can convert the annotation format to the expected format above and save it to
- a pickle file, like [pascal_voc.py](tools/convert_datasets/pascal_voc.py).
+ a pickle or json file, like [pascal_voc.py](tools/convert_datasets/pascal_voc.py).
Then you can simply use `CustomDataset`.
## Technical details
--
GitLab