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I am new in the deep learning, and I want to use convolutional neural networks (CNN) for image recognition (biometric images).
I would like to use pre-trained CNN architecture and use a python programming language.
How can I select the suitable CNN architecture (VGGNet or GoogleNet ...), is there a preferable CNN architecture?
What do you think is the best library to do this work, how can I select the suitable library?
Thanks..
You can use tensorflow-slim. They have a library of many top pre-trained CNN models that you can use directly or fine-tune easily on your dataset. I think the training time depends on your hardware and amount of data you have.
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I want to use pretrained Alexnet for transfer learning. I dont see its available in Keras library.
Am I missing something here?
Other Alternative I see here is to create model and
load pretrained weight
train from scratch
Training from scratch using imagenet dataset is not possible for me due to resource constraint.
Loading pre-trained weight will work.
Would you provide any pointers for getting the pretrained weight for Alexnet?
Thanks,
As of right now, Keras does not (officially) seem to offer a pre-trained AlexNet model. PyTorch, on the other hand, does. If you are willing to use a different framework for the task, you can use PyTorch. You can retrieve a pre-trained version of the AlexNet like so:
import torchvision.models as models
alexnet = models.alexnet(pretrained=True)
You can find the list of available pre-trained models here, and a transfer learning tutorial for image classification here.
Hope that answers your question!
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I want to use a pre-trained Unet model using segmentation_models API for the Cityscapes dataset, but I need the pre-trained weights for the same. Where can I find the pre-trained weights for a Unet model trained on the Cityscapes dataset?
Please guide me on this!!!
UNet is absent from the benchmark so i assume it is not adapted for this dataset (too slow and not enough performant probably). However, I advise you to start with DeepLabv3+ from Google which is not so complicated and more adapted for this dataset.
You can use this repository where it is implemented, well documented and useable with pretrained weights from cityscape dataset (and also PascalVOC dataset).
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If one image is a part of another image, then how to compute the accurate location in deep learning way?
Now I could compute this by extracting and matching key points using OpenCV, but I hope to solve it with neural networks.
Any ideas to design the networks and loss functions?
Thanks very much.
This is a detection problem. The simplest approach to do it is to create a a network with two heads, one for classification and the other for the bounding box (regression).
you feed your network with the image and respective label, and sum the lossess and do a backward. train for some epochs and you'll get your self a detection model that you can use to detect what you need. but its just a simple approach and it can get much more complex.
You may as well skip this and use an existing detection architecture or better framework which simplifies your life much better.
For Tensorflow I belive you can use ObjectDetctionAPI and for Pytorch you can use Detectron, Detectron2, mmdetection among others.
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I am trying to create Deep Neural Network based classifier for chest x-ray to check there is TB or not. I read that transfer Learning technique can be used for this using inception model v3. My question is inception model is created by training with imagenet(physical object) right? How can this be used for medical image training?
One intuition is that physical objects and medical images do share some similarities especially in low-level features such as edges, curves and small object regions.
Experiments indicate that pretraining a network on ImageNet can benefit most computer vision tasks even if the images from the target domain look very different from what are in the ImageNet.
To achieve best performance, you can use a pretrained network from Imagenet and fine-tune the last layer or all layers with small learning rates on your dataset.
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I am new to machine learning and I want to start doing basic regression analysis. I saw that scikit-learn provides a simple way to do this. But why people use tensorflow for regression instead? Thanks!
If the only thing you are doing is regression, scikit-learn is good enough and will definitely do you job. Tensorflow is more a deep learning framework for building deep neural networks.
There're people using Tensorflow to do regression maybe just out of personal interests or they think Tensorflow is more famous or "advanced".
Tensorflow is a deep learning framework and involves far more complex decisions concerning algorithm design.
In the first step, it is recommended to use sklearn, because you will get a first ml model with scikit-learn faster. Later you can use a dl model with tensorflow. :-)