I want to implements a faster-rcnn model using distributed tensorflow, But I have difficult to load a pretrained vgg model,How to do it? thanks
The TensorFlow tutorial on retraining inception is a good start to read. Then try to reproduce what it does starting from an already trained vgg model.
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I am using deepLab to generate semantic segmentation masked images for a video in cityscapes datasets. So, I started with the pre-trained model xception65_cityscapes_trainfine provided on the modelzoo and trained it further on the dataset.
I am curious to know How I can start training it from scratch? and not end up just using the pre-trained model? could anyone suggest a direction on How I can achieve it?
Any contribution from the community will be helpful and appreciated.
I need to Load a pre-trained model into Keras without the Weight and bias. I
also just want to use the Architecture of the model alone for my training.
Example:
I want to load coco_mobilenet model pre-trained without Weights and bias.
Any suggestions would be appreciated.
net=keras.applications.MobileNet(weight=None)
net.summary()
view keras mobilenet api for detail
Can someone point me to the Resnet34 pre-trained model on image-net using tensorflow? I am not sure but TF-slim trained model are same or would there be difference?
You can use Keras ResNet(18,34,50,101,152) pre-trained models https://github.com/qubvel/classification_models
I am trying to use the Inception SSD model in tensorflow object detection API. To initialize the weights i want to use pretrained Inception V2 On image net as the feature extractor. I see the model config file lets you use a pretrained model on COCO but if I want to use an Image net model how should I go about it?
To train on Imagenet classification models, do the following:
1) Download a pre-trained model from the "Pre-trained models" section on the Slim page
2) Point the fine_tune_checkpoint at that directory
3) Set from_detection_checkpoint to be false (as you will now be fine-tuning from a classification checkpoint)
Note that training from an Imagenet classification checkpoint will require significantly more time.
Can anyone give an example of how to fine tune a pretrained imagenet network with new data and different classes similar to this:
Fine-tuning a Pretrained Network for Style Recognition
This TensorFlow tutorial describes how to retrain a image classifier for new data and new classes.