From 0613aedb7c4ee05b65893343fbea6a34b8aac77e Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Sun, 21 Oct 2018 01:43:47 +0800
Subject: [PATCH] update readme and version
---
README.md | 46 ++++++++++++++++++++++++++++++++++++++++
mmdet/datasets/custom.py | 2 +-
setup.py | 2 +-
3 files changed, 48 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 4eb1f1b..47df2d4 100644
--- a/README.md
+++ b/README.md
@@ -34,6 +34,10 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Updates
+v0.5.2 (21/10/2018)
+- Add support for custom datasets.
+- Add a script to convert PASCAL VOC annotations to the expected format.
+
v0.5.1 (20/10/2018)
- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured.
- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency.
@@ -209,6 +213,48 @@ Expected results in WORK_DIR:
> 1. We recommend using distributed training with NCCL2 even on a single machine, which is faster. Non-distributed training is for debugging or other purposes.
> 2. The default learning rate is for 8 GPUs. If you use less or more than 8 GPUs, you need to set the learning rate proportional to the GPU num. E.g., modify lr to 0.01 for 4 GPUs or 0.04 for 16 GPUs.
+### Train on custom datasets
+
+We define a simple annotation format.
+
+The annotation of a dataset is a list of dict, each dict corresponds to an image.
+There are 3 field `filename` (relative path), `width`, `height` for testing,
+and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields:
+`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide
+annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore`
+to cover them.
+
+Here is an example.
+```
+[
+ {
+ 'filename': 'a.jpg',
+ 'width': 1280,
+ 'height': 720,
+ 'ann': {
+ 'bboxes': <np.ndarray> (n, 4),
+ 'labels': <np.ndarray> (n, ),
+ 'bboxes_ignore': <np.ndarray> (k, 4),
+ 'labels_ignore': <np.ndarray> (k, 4) (optional field)
+ }
+ },
+ ...
+]
+```
+
+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).
+
+- 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).
+ Then you can simply use `CustomDataset`.
+
## Technical details
Some implementation details and project structures are described in the [technical details](TECHNICAL_DETAILS.md).
diff --git a/mmdet/datasets/custom.py b/mmdet/datasets/custom.py
index 3ae4704..3640a83 100644
--- a/mmdet/datasets/custom.py
+++ b/mmdet/datasets/custom.py
@@ -271,4 +271,4 @@ class CustomDataset(Dataset):
data = dict(img=imgs, img_meta=img_metas)
if self.proposals is not None:
data['proposals'] = proposals
- return data
\ No newline at end of file
+ return data
diff --git a/setup.py b/setup.py
index 1803b73..9115195 100644
--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@ def readme():
MAJOR = 0
MINOR = 5
-PATCH = 1
+PATCH = 2
SUFFIX = ''
SHORT_VERSION = '{}.{}.{}{}'.format(MAJOR, MINOR, PATCH, SUFFIX)
--
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