Python Notes

zip: returns a list of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables.
a = [1, 2, 3]
b = ['a', 'b', 'c']
for i, j in zip(a, b):
     print(i,j)
=>1 a
=>2 b
=>3 c
Python __repr__() function returns the object representation. It could be any valid python expression such as tuple, dictionary, string etc. 

 id(object): return the "identity" of an object. This is an integer which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value.

 hasattr()
The hasattr() method returns true if an object has the given named attribute and false if it does not.

 hasattr(object, name)

 PIL.Image.resize(size, resample = 0)
returns a resized copy of this image

 Parameters: 
-size: the requested size in pixels, as a 2-tuple
-resample: an optional resampling filter. This can be one of PIL.Image.NEAREST(use nearest neighbour), PIL.Image.BILINEAR(linear interpolation in a 2x2 environment), PIL.Image.BICUBIC(cubic spline interpolation in a 4x4 environment) or PIL.Image.ANTIALIAS(a high-quality downsampling filter). If omitted, or if the image has mode "1" or "P", it is set PIL.Image.NEAREST.
return an Image object.

 np.prod(a, axis = None)
returns the product of array elements over a given axis

 isinstance(object, type)
returns true if the specified object is of the specified type, otherwise false
If the type parameter is a tuple, this function will return true if the object is one of the types in the tuple.

PIL: python image library
np.cumsum(a, axis = None)
return the cumulative sum of the elements along a given axis

 Mahotas: Computer Vision in Python
Mahotas is a computer vision and image processing library for Python.
np.ravel(a, order='C')
return a contiguous flattened array. 
parameters: a: array_like, input array, the elements in a are read in the order specified by order and packed as a 1-D array.
order: {'C', 'F', 'A', 'K'}, optional
'C': index the elements in row-major
'F': index the elements in column-major













Comments

Post a Comment

Popular posts from this blog

Reading CLIP

Learning directly from raw text about images is promising which leverages a much broader source of supervision. Task: predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of pairs. After pre-training, natural language is used to is used to reference learned visual concepts enabling zero-shot transfer of the model to downstream tasks.   [Paper Source]  https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. and Krueger, G., 2021. Learning transferable visual models from natural language supervision.  arXiv preprint arXiv:2103.00020 .
Read more

Reading CutPaste

 CutPaste: a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Dataset: MVTec anomaly detection dataset Aim: detect various types of real-world defects [ Code ] Paper Source:  https://openaccess.thecvf.com/content/CVPR2021/papers/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.pdf
Read more

OOD-related papers

Deep One-Class Classification, ICLR 2021 [ Paper ] [ Code ] Google AI [ Blog ] [ Code ] Learning from Failure: Training Debiased Classifier from Biased Classifier [ Paper ] [ Code ] NeurIPS 2020 Learning Debiased Representation via Disentangled Feature Augmentation [ Paper ] [ Code ] NeurIPS 2021 Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization [ Paper ] Explainable Deep One-class Classification [ Paper ] GAN Ensemble for Anomaly Detection [ Paper ] Neural Transformation Learning for Deep Anomaly Detection Beyond Images [ Paper ] Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection Dual Compositional Learning in Interactive Image Retrieval [*] SSL  Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity [ Paper ] [ Code ] NeurIPS 2021 Unsupervised Learning of Discriminative Attributes and Visual Representations [ Paper ] CVPR2016 Common Visual Pattern Dis...
Read more