I am looking for the model file definition for a resnet implementation using the mxnet gluon api. The model zoo only provides pre-trained models:
https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html
I am looking for the code implementing the resent model.
Here is the example from mxnet github repo:
https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/resnet.py
Related
TensorFlow Hub is a repository for pre-trained models. Model Garden (Model Zoo) also keeps SOTA models and provides facilities for downloading and leveraging its models like TfHub, and both of them are created by TensorFlow.
Why did Tensorflow make two concepts for a model repository?
When should we use TfHub for retrieving a well-known model, and when should we use Model Garden to download a model? What is the difference between them?
TF Hub provides trained models in SavedModel, TFLite, or TF.js format. These artifacts can be used for inference and some can be used in code for fine-tuning. TF Hub does not provide modeling library code to train your own models from scratch.
Model Garden is a modeling library for training BERT, image classification models, and more. Model Garden provides code for training your own models from scratch as well as some checkpoints to start from.
Is there any way to build object detection model using Tensorflow without any help of tf.keras module?
From Tensorflow documentation I'm not able to find any example which helps to create model without Keras.
Keras is a high level API. But if you want to use only Tensorflow then you have to implement the architecture using low level API. You can certainly implement but you have to code it yourself to build all the convolutional layers and dense layer by yourself.
This tutorial discusses how to use objection detection API at tensorflow.
I am looking for the tutorial explaining how to add feature extractor such as mobilenetV2 to tensorflow's object detection framework.
Have you checked out the Tensorflow provided Model Zoo? :)
It includes various object detection models with various feature extractors such as MobileNet, Inception, ResNet etc.
Here below you will find a link to the Tensorflow Detection Model Zoo, where you can choose detection model architectures, Region-Based (R-CNN) or Single Shot Detector (SSD) models, and feature extractors.
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
You can download a frozen graph of the pre-trained models based on COCO, Kitti and Open-Images etc.
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 used Tensorflow Object Detection API suuccessfuly by using ssd_mobilenet_v1_coco_11_06_2017 model.
Now I need to use Inception v3 model instead of mobilenet model.
Question:
can I use it for Tensorflow Object Detection API and how can I change the config file and how to find it?
Simply you can refer ssd_mobilenet_v1_coco.config file. it is used to give configuration for the Tensorflow object detection API. you can edit it and give modifications as you want.