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package:caffemodel2-bvlc-alexnet (v1.0.0)
License: unrestricted (see readme.md)
Creation date: 2017-05-14
Source: GitHub
cID: 1dc07ee0f4742028:56aa9e005f933b89

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Description  

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!

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ReadMe  


name: BVLC AlexNet Model caffemodel: bvlc_alexnet.caffemodel caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel license: unrestricted sha1: 9116a64c0fbe4459d18f4bb6b56d647b63920377 caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077


This model is a replication of the model described in the AlexNet publication.

Differences: - not training with the relighting data-augmentation; - initializing non-zero biases to 0.1 instead of 1 (found necessary for training, as initialization to 1 gave flat loss).

The bundled model is the iteration 360,000 snapshot. The best validation performance during training was iteration 358,000 with validation accuracy 57.258% and loss 1.83948. This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)

This model was trained by Evan Shelhamer @shelhamer

License

This model is released for unrestricted use.

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