HTML
files that are saved to the galleries
sub-dir of the work directory and presented interactively when using Jupyter notebooks.
Starting from V1.0 galleries have a new layer of abstraction that automatically adds bounding boxes and labels to images where available.
Fastdup.vis.component_gallery
Fastdup.vis.duplicates_gallery
Fastdup.vis.outliers_gallery
Fastdup.vis.stats_gallery
Fastdup.vis.similarity_gallery
slice
: Visualize a subset of the data with the given label, e.g., slice='dog'
sort_by
: Sort images by a property, supported are:
comp_size
- Number of images in the componentdistance
- The average distance between cluster members. Clusters where the images are most similar will be presented firstarea
- From the largest to the smallest image or bounding box average sizelabel_col
: Column to use as labels, common options are label
, split
and img_filename
.
num_images
: (default=20) The number of images to visualize.
max_width
: (default=None) Pixel width of displayed gallery. Useful values are often in the 800-1200 range.
lazy_load
: (default=False) When False
, images are embedded into the gallery HTML
files. Otherwise images are loaded by the browser using their relative paths. Using lazy_load
makes galleries lighter and faster to generate, but less portable and shareable. On the other hand, Without lazy loading galleries become very large files.
fd.vis.component_gallery()
. The rest of the parameters are optional, and could be selected in hindsight.
label_col
argument controls the labels appended to each image visualized. By default it fetches labels from the label column in the annotations dataframes provided during the fastdup.run()
call. When labels are not provided, or if the use of another column is desired, the label_col
argument could be set for using the required column.