Useful machine-learning related stuff.
Project description
Typical usage often looks like this:
#!/usr/bin/env python import ml_utils as mlu mlu.imutils.resize_keep_aspect(img_arr):
Some functions for supporting machine learning, generally and caffe-specific. Currently just a dump, ideally will clean up and add install.py, examples etc
caffe - python layers for
image read (file or lmdb) - single / multi label, pixel level labels
on-the-fly augmentation
support for pixel-level segmentation (read mask as label, etc)
controlling and reporting acc/loss of solver with solver.step
image processing
image augmentation - including bounding boxes and pixel level
acccuracy/precision/recall reporting for single label, multilabel , bounding box, and pixel level
utilities e.g. read from anywhere (url/db/local file/img array)
grabcut
read/write various file formats
lmdb, hd5
yolo
deepfashion
tamara berg
ILSRVC
etc
tagging tools
multilabel
pixel level
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for nn_utils-0.1.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | da13f723bb3feb843999348b79fa38ceccab7d1b5076ddf46525fb9070a3070c |
|
MD5 | 75b819c411889162de2c421bee5db12d |
|
BLAKE2b-256 | dc9de71db8903643a187b55310289bcd6175bbba7cae6547d9511a4334056cd2 |