Month: June 2021

I downloaded a software that takes images of people, and creates 3d models. I’m having an issue where the encodings fail, and I’m left with the message ‘tf.ConfigProto() AttributeError: module ‘tensorflow’ has no attribute ‘ConfigProto’

I have ZERO experience working with code/python so I’m utterly confused. I can post the full text if necessary. Been trying to fix this for hours

I’m working in a memory constrained environment and I’m trying to optimize memory usage as much as I can. Can I load train data alone or test data alone in a jupyter notebook?

I need to implement CosineSimilarity myself because i need to work on the individual losses before calculating the batch-wide mean.

I do it like this:  

a_n = tf.math.l2_normalize(a, axis=-1) 

b_n = tf.math.l2_normalize(b, axis=-1) 

d = -tf.math.reduce_sum(a_n * b_n, axis=-1) 

# Above is _identical_ to Keras' implementation. 

return d, tf.math.reduce_mean(d) 

I already compared the output to Keras’ implementation by repeatedly printing

 print(tf.math.reduce_sum(tf.math.abs(my_loss - keras_loss))) 

However, even though this outputs straight zeros (and never any NaNs), i still encounter NaNs, while with Keras’ implementation i do not. I already tried a higher epsilon in the l2_normalize, or using multiply_no_nan, to no avail.

Update: This comment.

Edit: Sorry, should have read the rules first. Mods, if you take this down because its not tensorflow specific, I understand.

I’m just starting to play with neural networks, object detection, and tracking. I’m wondering what people use the confidence score of a detection for. Are there any common uses beyond simple confidence thresholding (i.e. output detection if conf > 0.5, otherwise dont)? Papers that use the confidence value in interesting ways are welcome!

For my own project, I was wondering how I might use the confidence score in the context of object tracking. For fun, and because its a super common application, i’ve been playing around with a traffic sign detector, and deploying it in a simulation. In the simulation, I get consistent and accurate predictions for real signs, and then frequent but short lived (i.e. 1-3 frame lifetime) false positives. I was thinking I could do some sort of tracking that uses the confidence values over a series of predictions to compute some kind of detection probability. I.e. if i look at a series of 30 frames, and in 20 i have 0.3 confidence of a detection, where the bounding boxes all belong to the same tracked object, then I’d argue there is more evidence that an object is there than if I look at a series of 30 frames, and have 2 detections that belong to a single object, but with a higher confidence e.g. conf=0.6. How can I leverage the confidence scores to create a more robust detection and tracking pipeline? Or am I already way off base (i’ve been trying to come up with a formula for how to do it, but probability and stochastics were never my strong suit and I know that the formulas I’ve been trying to write down implicitly assume independence, which I don’t know if that is the case here)?

Any way, how do you use the confidence values in your own projects?

I am trying to convert a pretrained model (Efficientnet) which I have trained on some custom images and new labels. But when using tf2onnx to convert it to onnx format it requires a checkpoint.meta file? But I can’t see this file anywhere? I only see a .index and .data file from the model when I have trained it.

Many deep learning models created using TensorFlow require high processing capabilities to perform inference. Fortunately, there is a lite version of TensorFlow called TensorFlow Lite (TFLite for short) which allows these models to run on devices with limited capabilities. Inference is performed in less than a second.

This tutorial will go through how to prepare Raspberry Pi (RPi) to run a TFLite model for classifying images. After that, the TFLite version of the MobileNet model will be downloaded and used for making predictions on-device.

Tutorial video link: https://youtu.be/FdfxizUUQJI

Run the code on a free GPU: https://console.paperspace.com/ml-showcase/notebook/rljtgo7aadmiq7q?file=Raspberry%20Pi%20TF%20Lite%20Models.ipynb

This package makes it easy for us to create efficient image Dataset generator.

github link

Supported Augmentations

• standardize
• resize
• random_rotation
• random_flip_left_right
• random_flip_up_down
• random_shift
• random_zoom
• random_shear
• random_brightness
• random_saturation
• random_hue
• random_contrast
• random_crop
• random_noise