I am trying to use Tensorflow for transfer learning using a pre-trained VGG16 model.
However, the input to the model in my problem is an RGB image with an extra channel functioning as a binary mask. This is different than the original input on which the model was trained (224x224 RGB images).
I think that using the pretrained model is still possible in this case. How do I assign weights for connections between the first convolutional layer and the extra channel? Is transfer learning still applicable in such a scenario?
Thanks!
Related
SCENARIO
What if my intention is to train for a dataset of medical images and I have chosen a coco pre-trained model.
My Doubts
1 Since I have chosen medical images there is no point of train it on COCO dataset, right? if so what is a possible solution to do the same?
2 Adding more layers to a pre-trained model will screw the entire model? with classes of around 10 plus and 10000's of training datasets?
3 Without train from scratch what are the possible solutions , like fine-tuning the model?
PS - let's assume this scenario is based on deploying the model for business purposes.
Thanks-
Yes, it is a good idea to reuse the Pre-Trained Models or Transfer Learning in Real World Projects, as it saves Computation Time and as the Architectures are proven.
If your use case is to classify the Medical Images, that is, Image Classification, then
Since I have chosen medical images there is no point of train it on
COCO dataset, right? if so what is a possible solution to do the same?
Yes, COCO Dataset is not a good idea for Image Classification as it is efficient for Object Detection. You can reuse VGGNet or ResNet or Inception Net or EfficientNet. For more information, refer TF HUB Modules.
Adding more layers to a pre-trained model will screw the entire model?
with classes of around 10 plus and 10000's of training datasets?
No. We can remove the Top Layer of the Pre-Trained Model and can add our Custom Layers, without affecting the performance of the Pre-Trained Model.
Without train from scratch what are the possible solutions , like
fine-tuning the model?
In addition to using the Pre-Trained Models, you can Tune the Hyper-Parameters of the Model (Custom Layers added by you) using HParams of Tensorboard.
I am looking to train a large face identification network. Resnet or VGG-16/19. TensorFlow 1.14
My question is - if I run out of GPU memory - is it valid strategy to train sets of layers one by one?
For example train 2 cnn and maxpooling layer as one set, then "freeze the weights" somehow and train next set etc..
I know I can train on multi-gpu in tensorflow but what if I want to stick to just one GPU..
The usual approach is to use transfer learning: use a pretrained model and fine-tune it for the task.
For fine-tuning in computer vision, a known approach is re-training only the last couple of layers. See for example:
https://www.learnopencv.com/keras-tutorial-fine-tuning-using-pre-trained-models/
I may be wrong but, even if you freeze your weights, they still need to be loaded into the memory (you need to do whole forward pass in order to compute the loss).
Comments on this are appreciated.
I have RGB-D (color&depth) images for given scene. I would like to use tensorflow to train a classification model based on pre-trained network such as inception. As far as I understood, these pre-trained models were built using 3-channel RGB images. However, the inclusion of 4th channel cannot be handled.
How do I use RGB-D images directly? Do I need to pre-process the images, and seperate RGB and D, if so, how do I use the D (1-channel) alone?
Thank you!
If you want to use a pre-trained model you can only use RGB, as they were only trained to understand RGB. In this case, it is as you said: separate them and discard depth.
To use a 4 channel image like this you would need to retrain the network from scratch rather than loading a pre-trained set of weights.
You will probably get good results using the same architecture as is used for 3 channel images (save for the minor change required to support the 4 channel input), so retraining shouldn't be terribly hard.
I have implemented a form of the LeNet model via tensorflow and python for a Car number plate recognition system. My model was trained solely on my train data and tested on the test data. My dataset contains segmented images wherein every image has only one character in them. This is what my data looks like. My created model does not perform very well, so I'm now looking for models which I can use via Transfer Learning. Since most models, are already trained on a humongous dataset, I looked over a few like AlexNet, ResNet, GoogLeNet and Inception v2. Most of these models have not been trained on the type of data that I want which would be, Letters and digits.
Question: Should I still go forward with one of these models and train them on my dataset or are there any better models which would help ? For such models would keras be a better option since it is more high level than Tensorflow?
Question: I'd prefer to work with the LeNet model itself since training the other models would definitely take a long time due to the insufficient specs of my laptop. So is there any implementation of the model which uses machine printed character images to train the model which I could use to then train the final layers of the model on my data?
to get good results you should use a model explicitly designed for text recognition.
First, (roughly) crop the input image to the region around the text.
Then, feed the image of the text into a neural network (NN) to detect the text.
A typical NN for text recognition extracts relevant features (with convolutional NN), propagates those features through the image (with recurrent NN) and finally predicts a character score for each position in the image.
Usually, those networks are trained with the CTC loss.
As a starting point I would suggest looking at the CRNN implementation (they also provide a pre-trained model) [1] and the corresponding paper [2]. There is, as far as I remember, also a TensorFlow implementation on github.
You can use any framework (e.g TensorFlow or CNTK or ...) you like as long as it features convolutional and recurrent NN and the CTC loss.
I once attended a presentation about CNTK where they claimed that they have a very fast implementation of recurrent NN - so maybe CNTK would be a good choice for your slow computer?
[1] CRNN implementation: https://github.com/bgshih/crnn
[2] Shi - An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
I am learning convolutional networks in Tensorflow. I wonder if there is any tutorials of using TF to investigate a pre-trained convnet model, like these excellent tutorials for Caffe: this and this. I mean, how to access middle layers, get its learned parameters and blobs, to customize input shape to accept arbitrary image size or batch size, etc.
It's not quite the same thing, but there's a codelab here that shows you how to remove the top layer of a pretrained network and train up a new one on your own data:
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html?index=..%2F..%2Findex#0
It might give you some ideas on how to approach this in TensorFlow.