Wheelodex » Projects » kerascv

kerascv

View on PyPI — Reverse Dependencies (0)

0.0.23 kerascv-0.0.23-py2.py3-none-any.whl

Wheel Details

Project: kerascv
Version: 0.0.23
Filename: kerascv-0.0.23-py2.py3-none-any.whl
Download: [link]
Size: 81728
MD5: 84556a8a6622846c171ca791910caa45
SHA256: 10a8aeced2392f024f9f4ce101f04363915d8bf5cf7ddaadee74cfad7a1f2541
Uploaded: 2019-02-18T22:50:45

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: kerascv
Version: 0.0.23
Summary: Image classification models for Keras
Author: Oleg Sémery
Author-Email: osemery[at]gmail.com
Home-Page: https://github.com/osmr/imgclsmob
License: MIT
Keywords: machine-learning deep-learning neuralnetwork image-classification imagenet keras keras-mxnet vgg resnet resnext senet densenet darknet squeezenet squeezenext shufflenet menet mobilenent igcv3 mnasnet
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Dist: keras
Requires-Dist: h5py
Description-Content-Type: text/markdown
[Description omitted; length: 22652 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.32.3)
Root-Is-Purelib: true
Tag: py2-none-any
Tag: py3-none-any

RECORD

Path Digest Size
kerascv/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
kerascv/model_provider.py sha256=w20QtZbHxpuqoKuXZ8k-27TvSXbtt14ze_S-RuvzdLQ 6135
kerascv/models/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
kerascv/models/alexnet.py sha256=w_Ie1X9IuOFa20QeiPkcNLyxoxFdZ8xCiLpeS-hoU6s 8051
kerascv/models/common.py sha256=B2GLX7sth4-Zs69B2917e6ugmgzIXQis7AP60DfG0JU 38898
kerascv/models/darknet.py sha256=8q1ORumuuEPyTYRF4Mn1Kz7t4rLIuMu9OI2XrbylXx8 8310
kerascv/models/darknet53.py sha256=QyHIZyeXSjIQ6LbOpYS-uT9W-Hyjc5pMlbU8DwfQ3f0 6663
kerascv/models/densenet.py sha256=g2TctLLmvb_zrzSCTLu-do4DRb7Jv1kzakztSn9otf0 10110
kerascv/models/igcv3.py sha256=-ZtGedTWm76bYZy0lqp9fCDkMKWj729dygR4mTE5V-E 9725
kerascv/models/menet.py sha256=LAK9L_ugLpl1qMreZ2zSI8f4M_BVbiaDHC52J1f9gLo 15937
kerascv/models/mnasnet.py sha256=r1Z1JNcCRS_Wz5OCDQyHO35adVJwR1zEC5QkYhFKv2E 14216
kerascv/models/mobilenet.py sha256=Ueam_gY5GIZqYyseCo525fn0Fnud-RQaKgTR5aCD2_8 11413
kerascv/models/mobilenetv2.py sha256=7vCNc9OTj81oleW1Xc5nU5vuHnPU3v_ZRufCMbI66oY 9595
kerascv/models/model_store.py sha256=3wBVpS95YxMVaGd_GIX6fgdRGVVTaJbeaxsPeavQZrk 22594
kerascv/models/preresnet.py sha256=1Op3ARUzVm0Ql8GM1p0nEJdwTGdPlGfkrO1nJI-9kyI 22808
kerascv/models/resnet.py sha256=K3LsoXkKZH8J7dzo190v7ulHVUlrpEakVkuwzzZ8kd4 21619
kerascv/models/resnext.py sha256=rpO4Mnbwk_sYq9Smrtnt2MXY2ot-uAh7hjAfRdop1AU 10188
kerascv/models/senet.py sha256=EDLN1i8G1MqSrjcNtnsV-mKOwuDxXRYJr_6dk3bt4Mk 11609
kerascv/models/sepreresnet.py sha256=B17sSOgE8cBCQ_1qBk6OKbbsefW2SrFNUwTKJibP5u4 13585
kerascv/models/seresnet.py sha256=wxCB3Ov3edJl7cEOmIVD_JxLEB262ruy24cPGcEtMBw 13247
kerascv/models/seresnext.py sha256=voTcLlvPdaZbh9UnIgvXXFASD7wrdOTa7-it5Dz975A 8604
kerascv/models/shufflenet.py sha256=4E-jtJJnce587aOvr6onul63MHu5z4zYB7zLUQAeuow 15960
kerascv/models/shufflenetv2.py sha256=TfGTb00nLRTBx5KS8lXguwMpP2owuJ7BuKm3Aw9LPwA 12061
kerascv/models/shufflenetv2b.py sha256=w9YxORpBp5zp9mKdnrcFwjJcvQK4KnUwfIoHFO5aNzs 12357
kerascv/models/squeezenet.py sha256=h8aW3dQo9QyNY_ZUEeL2gV0_hcw_jBjAOZwAYejn-fw 12333
kerascv/models/squeezenext.py sha256=c7xwVFEtcqAztK82XFEzRx9YU0aUEI41-0b8rT3tr6Q 12310
kerascv/models/vgg.py sha256=Hn5AFslp4zcCHK9rwdGW52bA2J2IZQA-Q9Q0HhTu6Ag 16326
kerascv-0.0.23.dist-info/LICENSE.txt sha256=Wr0V_EGF_rLAvGs1Fs8X5Rx2R_CTYg-fncwPnsp-CJI 1090
kerascv-0.0.23.dist-info/METADATA sha256=fu-yZqq2M8184knGedstOY--u-l3WtZTuuGZeLuMyb8 23472
kerascv-0.0.23.dist-info/WHEEL sha256=aEiWNTQcG6AiGGAYQ9qHTifyKRnmFJvDeTI0hP5aohY 116
kerascv-0.0.23.dist-info/top_level.txt sha256=s5Kzj9KmEFXNKM-l3VJTgWoB0u51dZhhfUp4o2CJpgg 8
kerascv-0.0.23.dist-info/RECORD — —

top_level.txt

kerascv