diff --git a/icedata/datasets/pennfudan/README.md b/icedata/datasets/pennfudan/README.md index 2d86d95..ee52fa5 100644 --- a/icedata/datasets/pennfudan/README.md +++ b/icedata/datasets/pennfudan/README.md @@ -12,7 +12,7 @@ There are 170 images with 345 labeled pedestrians, among which 96 images are tak ![image](images/pennfudan_annotations.jpg) ## Usage -Open In Colab Example showing how to use this dataset +Open In Colab Example showing how to use this dataset ## How to load this dataset @@ -22,19 +22,19 @@ from icevision.all import * import icedata # Load the PennFudan dataset -path = icedata.penfundan.load_data() +path = icedata.pennfudan.load_data() ``` ## How to parse this dataset ```python # Get the class_map, a utility that maps from number IDs to classs names -class_map = icedata.penfundan.class_map() +class_map = icedata.pennfudan.class_map() # Randomly split our data into train/valid data_splitter = RandomSplitter([0.8, 0.2]) # PennFudan parser: provided out-of-the-box -parser = icedata.penfundan.parser(data_dir=path, class_map=class_map) +parser = icedata.pennfudan.parser(data_dir=path, class_map=class_map) train_records, valid_records = parser.parse(data_splitter) # shows images with corresponding labels and boxes @@ -43,8 +43,8 @@ show_records(train_records[:6], ncols=3, class_map=class_map, show=True) ## How to load the pretrained weights of this dataset ```python -class_map = icedata.penfundan.class_map() -model = icedata.penfundan.trained_models.faster_rcnn_resnet50_fpn() +class_map = icedata.pennfudan.class_map() +model = icedata.pennfudan.trained_models.faster_rcnn_resnet50_fpn() ``` ## Dataset folders @@ -78,4 +78,4 @@ Please, check out [here](https://www.cis.upenn.edu/~jshi/ped_html/) [^1]: Object Detection Combining Recognition and Segmentation. Liming Wang, Jianbo Shi, Gang Song, I-fan Shen. - ACCV 2007 pp 189-199 \ No newline at end of file + ACCV 2007 pp 189-199