I am working on anomaly detection model (for PCBs) using Autoencoder , I am working on google Colab using free GPU. so as a first step I was trying to build my autoencoder and visualise the reconstruction of my training data(pictures without defects size 1,3 MP). I built a model of three layers with 150 epochs batch size =2, it gave me good results. I used SSIM loss function to calculate the difference between the test photos ( pictures with aomalies) and the training data(pictures without anomalies). The problem here that I want to visualize these differences with the HeatMap as I read in some articles that it is possible to localize anomalies in a pixel level .. I suppose it is related to the loss function that we use to calculate the difference.
do you have any idea what functions could help me visualize/Localize anomalies ?
The task of outputting where in an image is known as Anomaly Localization. There are many academic papers on the topic for advanced methods.
When using a reconstructing autoencoder on images for anomaly detection, one can compute the difference between the input image and the reconstructed output image as an anomaly-level image.
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I am training a yolov4 (fully convolutional) in tensorflow 2.3.0.
I would like to change the spatial input shape of the network during training, to further adjust the weights to different scales.
Is this possible?
EDIT:
I know of the existence of darknet, but it suffers from some very specific augmentations I use and have implemented in my repo, that is why I ask explicitly for tensorflow.
To be more precisely about what I want to do.
I want to train for several batches at Y1xX1xC then change the input size to Y2xX2xC and train again for several batches and so on.
It is not possible. In the past people trained several networks for different scales but the current state-of-the-art approach is feature pyramids.
https://arxiv.org/pdf/1612.03144.pdf
Another great candidate is to use dilated convolution which can learn long distance dependencies among pixels with varying distance. You can concatenate the outputs of them and the model will then learn which distance is important for which case
https://towardsdatascience.com/review-dilated-convolution-semantic-segmentation-9d5a5bd768f5
It's important to mention which TensorFlow repository you're using. You can definitely achieve this. The idea is to keep the fixed spatial input dimension in a single batch.
But even better approach is to use the darknet repository from AlexeyAB: https://github.com/AlexeyAB/darknet
Just set, random = 1 https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov4.cfg [line 1149]. It will train your network with different spatial dimensions randomly.
One thing you can do is, start your training with AlexeyAB repo with random=1 set, then take the trained weights file to tensorflow for fine-tuning.
I am working on object detection model to identify two classes. I am using Faster RCNN on customized dataset in tensorflow api. The dataset contains 20k images (augmented) with two classes. While training the model the loss is not decreasing properly as it reach to 100k steps. It has lot of variation as shown in image. Can someone tell me where i am making mistake.
enter image description here
I'm new in everithing about CNN and tensorflow. Im training a pretrained ssd-mobilenev1-pets.config to detect columns of buildings, about one day but the loss is between 2-1 and doesnt decrease since 10 hours ago.
I realized that my input images are 128x128 and SSD resize de image to 300*300.
Does the size of the input images affect the training?
If that is the case, should I retrain the network with larger input images? or what would be another option to decrease the loss? my train dataset has 660 images and test 166 I dont Know if there are enough images
I really aprecciate your help ....
Loss values of ssd_mobilenet can be different from faster_rcnn. From EdjeElectronics' TensorFlow Object Detection Tutorial:
For my training on the Faster-RCNN-Inception-V2 model, it started at
about 3.0 and quickly dropped below 0.8. I recommend allowing your
model to train until the loss consistently drops below 0.05, which
will take about 40,000 steps, or about 2 hours (depending on how
powerful your CPU and GPU are). Note: The loss numbers will be
different if a different model is used. MobileNet-SSD starts with a
loss of about 20, and should be trained until the loss is consistently
under 2.
For more information: https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#6-run-the-training
The SSD Mobilnet architecture demands additional training to suffice
the loss accuracy values of the R-CNN model, however, offers
practicality, scalability, and easy accessibility on smaller devices
which reveals the SSD model as a promising candidate for further
assessment (Fleury and Fleury, 2018).
For more information: Fleury, D. & Fleury, A. (2018). Implementation of Regional-CNN and SSD machine learning object detection architectures for the real time analysis of blood borne pathogens in dark field microscopy. MDPI AG.
I would recommend you to take 15%-20% images for testing which cover all the variety present in training data. As you said you have 650+ images for training and 150+ for testing. That is roughly 25% of testing images. It looks like you have enough images to start with. I know the more, the merrier but make sure your model also has sufficient data to learn from!
Resizing the images does not contribute to the loss. It makes sure there is consistency across all images for the model to recognize them without bias. The loss has nothing to do with image resizing as long as every image is resized identically.
You have to make stops and recover checkpoints again and again if you want your model to be perfectly fit. Usually, you can get away with good accuracy by re-training the ssd mobilenet until the loss consistently becomes under 1.Ideally we want the loss to be as lower as possible but we want to make sure the model is not over-fitting. It is all about trial and error. (Loss between 0.5 and 1 seems to be doing the job well but again it all depends on you.)
The reason I think your model is underperforming is due to the fact that you have variety of testing data and not enough training data to suffice.
The model has not been given enough knowledge in training data to make the model learn for new variety of testing data. (For example : Your test data has some images of new angles of buildings which are not sufficiently present in training data). In that case, I recommend you to put variety of all images in training data and then picking images to test making sure you still have sufficient training data of new postures. That's why I recommend you to take 15%-20% test data.
[![enter image description here][1]][1]I am actually reconstructing some images using dual photography. Next, I want to train a network to reconstruct clear images by removing noise (Denoising autoencoder).
The input for training the network is reconstructed images, whereas, the output is ground truth or computer based standard test images. Now the input e.g., Lena is some how not exact version of Lena with image shifted in positions and some artifacts.
If I keep input as my reconstructed image and training output as Lena test image (computer standard test image) , will it work?
I only want to know if input/output shifted or some details missing in one of them (due to some cropping) would work.
It depends on many factors like your images for training and the architecture of the network.
However, what you want to do is to make a network that learns the noise or low level information and for this purpose Generative Adversarial Networks (GAN) are very popular. You can read about them here. Maybe, after you have tried your approach and if the results are not satisfactory then try using GANs, like, DCGAN (Deep Convolution GAN).
Also, share your outcomes with the community if you would like.
Denoising Autoencoders! Love it!
There is no reason for not training your model with those images. The autoencoder, if well trained, will eventually learn the transformation if there is enough data.
However, if you have the 'positive' images, I strongly recommend you to create your own noisy images and then train in that controlled working area. You will simplify your problem and it will be easier to solve.
What is stopping you from doing just that?
I have followed this Tensorflow tutorial on transfer learning with the Inception model using my own dataset of 640x360 images. My question comes in 2 parts
1) My data set conatains 640x360 images. Is the first operation that happens a downsampling to 299x299? I ask because I have a higher res version of the same dataset and I am wondering if training with the higher resolution images will result in different performance (hopefully better)
2) When running the network (using tf.sess.run()) is my input image down-sampled to 299x299?
Note: I have seen the 299x299 resolution stat listed many places online like this one and I am confused at exactly which images its referring to; the initial training dataset images (for Inception I think it was imagenet), the transfer learning dataset (my personal one), the input image when running the CNN, or a combination of the 3.
Thanks in advance :)
The inception model will resize your image to 299x299. This can be confirmed by visualizing the tensorflow graph. If you have enough samples to do the transfer learning, the accuracy will be good enough with resizing to 299x299. But if you really want to try out the training with actual resolution, the initial input layers of the graph size needs to be changed