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package:mxnetmodel-resnet-152 (v3.0.0)
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This meta package is our attempt to provide a unified Python API, CLI
and JSON meta description
for different package managers and building tools
to automatically download and install different components (models, data sets, libraries, frameworks, tools)
necessary to run portable program pipelines
across evolving platforms.
Our on-going project is to make the onboarding process as simple as possible via this platform.
Please check this CK white paper
and don't hesitate to contact us if you have suggestions or feedback!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!
Copyright: See copyright in the source repository
License: See license in the source repository
Creation date: 2017-10-26
Source: GitHub
cID: 1dc07ee0f4742028:2b1a670ef680b046
Don't hesitate to get in touch if you encounter any issues or would like to discuss this community project!
License: See license in the source repository
Creation date: 2017-10-26
Source: GitHub
cID: 1dc07ee0f4742028:2b1a670ef680b046
Description
- Automation framework: CK
- Development repository: ck-ml
- Source: GitHub
- Tags: mxnetmodel,resnet,resnet-152,channel-stable
- Support for host OS: any
- Support for target OS: any
- Version: resnet-152
- Software detection plugin: model.mxnet
- Template: MXNet model - ResNet152
- How to get the stable version from this portal via the CK client:
pip install cbench
cb download package:mxnetmodel-resnet-152 --version=3.0.0 --all
ck install package:mxnetmodel-resnet-152
ck help package
- How to get the development version:
pip install ck
ck pull repo:ck-ml
ck install package:mxnetmodel-resnet-152
ck help package
- Useful CK automation actions (CLI with the Python CK API and JSON IO):
ck show env
ck find env:{ID from above}
ck virtual env:{ID from above}
- CLI and Python API: module:package
- Useful CK automation actions (CLI with the Python CK API and JSON IO):
ReadMe
https://mxnet.incubator.apache.org/tutorials/python/predict_image.html http://data.mxnet.io/models/imagenet-11k/
We first download a pre-trained ResNet 152 layer that is trained on the full ImageNet dataset with over 10 million images and 10 thousand classes. A pre-trained model contains two parts, a json file containing the model definition and a binary file containing the parameters. In addition, there may be a text file for the labels.
Files
- .cm/desc.json (3 bytes)
- .cm/info.json (399 bytes)
- .cm/meta.json (1206 bytes)
- .cm/meta.json.old (1026 bytes)
- install.bat (301 bytes)
- install.sh (372 bytes)
- model.py (443 bytes)
- readme.md (444 bytes)