deepdetect | Deep Learning API and Server in C++14 support for Caffe | Machine Learning library

by jolibrain C++ Version: v0.24.0 License: Non-SPDX

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kandi X-RAY | deepdetect Summary

deepdetect is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. deepdetect has no bugs, it has no vulnerabilities and it has medium support. However deepdetect has a Non-SPDX License. You can download it from GitHub.

Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

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deepdetect has a medium active ecosystem.

It has 2453 star(s) with 558 fork(s). There are 128 watchers for this library.

It had no major release in the last 12 months.

There are 80 open issues and 341 have been closed. On average issues are closed in 45 days. There are 9 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deepdetect is v0.24.0

Quality

deepdetect has no bugs reported.

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deepdetect has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

deepdetect has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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deepdetect releases are available to install and integrate.

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Community Discussions

Trending Discussions on deepdetect

Using inception v4 in retrain example

QUESTION

Using inception v4 in retrain example

Asked 2019-Jul-23 at 19:48

I am trying to adapt the example retrain script ( https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py ) to use the Inception V4 model.

The script already supports the retraining of Inception V3 (2015) as well as different versions of Mobilenets.

What I've done so far: Since the script uses protobuf (.pb) files and not checkpoints (.ckpt), I downloaded the inception_v4.pb from here: https://deepdetect.com/models/tf/inception_v4.pb. As far as I understand, one could also have loaded the checkpoint and used the freeze graph tool to obtain the same file.

Then, I viewed the graph in tensorboard using the tensorflow python tool import_pb_to_tensorboard.py which can be found in the tensorflow github repository. From there (correct me if I am not wrong) I found that the resized_input_tensor_name is called InputImage whereas the bottleneck_tensor_name is InceptionV4/Logits/Logits/MatMul with bottleneck_tensor_size is 1001.

Having this information I tried to adapt the create_model_info(architecture) function of the retrain script by adding:

...

ANSWER

Answered 2019-Jul-23 at 19:48

I'm working through the same thing currently.

Try to add :0 to the end of your bottleneck_tensor_name and your resized_input_tensor_name.

If you'll notice in retrain.py, Google also uses this :0 nomenclature.

My suspicion is that, for you, InceptionV4/Logits/Logits/MatMul is just an operation, which you're not trying to get for this script, while InceptionV4/Logits/Logits/MatMul:0 is the first tensor instantiated from that operation, which you are trying to get for this script.

Source https://stackoverflow.com/questions/46829684

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