I would like to run the googlenet with tensorflow. Is there any multi-GPU version available that can be run with tensorflow?
You can train, evaluate and fine-tune an Inception v3 model. See links there-in for pointers.
http://googleresearch.blogspot.com/2016/03/train-your-own-image-classifier-with.html
Can you also please check out: https://github.com/tensorflow/models/tree/master/inception? (The image is from the URL.)
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How do I find more info on how the ssd_mobilenet_v1 tflite model on TFHub was trained?
Was it trained in such a way that made it easy to convert it to tflite by avoiding certain ops not supported by tflite? Or was it trained normally, and then converted using the tflite converter with TF Select and the tips on this github issue?
Also, does anyone know if there's an equivalent mobilenet tflite model trained on OpenImagesV6? If not, what's the best starting point for training one?
I am not sure about about the exact origin of the model, but looks like it does have TFLite-compatible ops. From my experience, the best place to start for TFLite-compatible SSD models is with the TF2 Detection Zoo. You can convert any of the SSD models using these instructions.
To train your own model, you can follow these instructions that leverage Google Cloud.
I am following a codelab tutorial by Google for image recognition:
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#3
However, in this case the tutorial is using MobileNet v1 for object detection. In fact, these env variables are set:
IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"
But what if I would like to use MobileNet with SSD or SquezeNet for object detection? I guess ARCHITECTURE variable must change in something like
ARCHITECTURE="ssd_mobilenet_0.50_${IMAGE_SIZE}"
I can't find any helpful resource.
The tutorial you are following is using this retrain script which is an older version of the official tensorflow retrain script.
While you can only use either MobileNet or InceptionV3 by using the codelab script, you can follow the official documentation on image retraining to retrain using any model available on Tensorflow Hub.
UPDATE:
MobileNet and SqueezeNet are not suitable for object recognition, but only for image classification. Thus, SSDMobileNet is the possible way.
I have trained DNNClassifier using Python (conda tensorflow installation). The trained model needs to be used for evaluation using C_API. Is there a way to load both graph and weights of the trained model using C_API?
There is a way to load h5 and any data for C_API. Maybe some googling could help. I've found this article to be helpful.
And for DNNClassifier on C_API I think you should Implement it manually using pure Tensor Array on C_API. cmiimw
I have models trained on tensorflow. Can I use mxnet in forward only mode to run these ?
https://github.com/dmlc/nnvm says this should be possible in future, but is the support available today ?
MXNet doesn't have tensorflow model converter yet. It does have a caffe-to-mxnet converter. So you can convert your tf model to caffe, that would work..
https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter
Are there any resnet implementations in tensorflow? I came across a few (e.g. https://github.com/ry/tensorflow-resnet, https://github.com/xuyuwei/resnet-tf) but these implementations have some bugs (e.g. see the Issues section on the respective github page). I am looking to train imagenet using resnet and looking for tensorflow implementations.
There are some (50/101/152) in tensorflow:models/slim.
The example notebook shows how to get a pre-trained inception running, res-net is probably no different.
I implemented a cifar10 version of ResNet with tensorflow. The validation errors of ResNet-32, ResNet-56 and ResNet-110 are 6.7%, 6.5% and 6.2% respectively. (You can modify the number of layers easily as hyper-parameters.)
I tried to be friendly with new ResNet fan and wrote everything straightforward. You can run the cifar10_train.py file directly without any downloads.
https://github.com/wenxinxu/resnet_in_tensorflow
I implemented Resnet by use of ronnie.ai and keras. Both of tool are great.
While ronnie is more easy from scratch.