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222_working_with_large_data_that_does_not_fit_memory_semantic_segm
222_working_with_large_data_that_does_not_fit_memory_semantic_segm
 
 
228_semantic_segmentation_of_aerial_imagery_using_unet
228_semantic_segmentation_of_aerial_imagery_using_unet
 
 
229_smooth_predictions_by_blending_patches
229_smooth_predictions_by_blending_patches
 
 
230_landcover_dataset_segmentation
230_landcover_dataset_segmentation
 
 
231_234_BraTa2020_Unet_segmentation
231_234_BraTa2020_Unet_segmentation
 
 
235-236_pre-training_unet_using_autoencoders
235-236_pre-training_unet_using_autoencoders
 
 
237_tflite_using_malaria_binary_classification
237_tflite_using_malaria_binary_classification
 
 
238_face_eye_detection_using_opencv
238_face_eye_detection_using_opencv
 
 
239_train_emotion_detection
239_train_emotion_detection
 
 
240_train_age_gender_detection
240_train_age_gender_detection
 
 
241_live_age_gender_emotion_detection
241_live_age_gender_emotion_detection
 
 
242 - Real time detection of facial emotion, age, and gender using TensorFlow Lite
242 - Real time detection of facial emotion, age, and gender using TensorFlow Lite
 
 
243 - Real time detection of facial emotion, age, and gender using TensorFlow Lite on RaspberryPi
243 - Real time detection of facial emotion, age, and gender using TensorFlow Lite on RaspberryPi
 
 
244-what_are_embedding_layers
244-what_are_embedding_layers
 
 
245 - Advantages of keras functional API in defining complex models
245 - Advantages of keras functional API in defining complex models
 
 
246 - Training a keras model by enumerating epochs and batches
246 - Training a keras model by enumerating epochs and batches
 
 
248_keras_implementation_of_GAN
248_keras_implementation_of_GAN
 
 
249_keras_implementation-of_conditional_GAN
249_keras_implementation-of_conditional_GAN
 
 
251_satellite_image_to_maps_translation
251_satellite_image_to_maps_translation
 
 
252_generating_realistic_scientific_images_using_pix2pix
252_generating_realistic_scientific_images_using_pix2pix
 
 
253_254_cycleGAN_monet2photo
253_254_cycleGAN_monet2photo
 
 
255_256_SRGAN
255_256_SRGAN
 
 
257 - Exploring GAN latent space to generate images with desired features
257 - Exploring GAN latent space to generate images with desired features
 
 
259_semi_supervised_GAN
259_semi_supervised_GAN
 
 
260_image_anomaly_detection_using_autoencoders
260_image_anomaly_detection_using_autoencoders
 
 
261_global_average_pooling
261_global_average_pooling
 
 
262_localizing_anomalies_in_images
262_localizing_anomalies_in_images
 
 
263_Object localization in images​_using_GAP_layer
263_Object localization in images​_using_GAP_layer
 
 
264 - Image outlier detection using alibi-detect
264 - Image outlier detection using alibi-detect
 
 
265_feature_engineering_or_deep_learning
265_feature_engineering_or_deep_learning
 
 
266_openslide_for_whole_slide_images
266_openslide_for_whole_slide_images
 
 
267_processing_whole_slide_images
267_processing_whole_slide_images
 
 
268-How to deploy your trained machine learning model into a local web application
268-How to deploy your trained machine learning model into a local web application
 
 
269_How to deploy your trained machine learning model as an app on Heroku
269_How to deploy your trained machine learning model as an app on Heroku
 
 
270-How to deploy your trained machine learning model as an app on Heroku-HAM10000-no docker
270-How to deploy your trained machine learning model as an app on Heroku-HAM10000-no docker
 
 
271-How to deploy your trained machine learning model as an app on Heroku-HAM10000-with docker
271-How to deploy your trained machine learning model as an app on Heroku-HAM10000-with docker
 
 
272-Instance segmentation via semantic segmentation by using border class
272-Instance segmentation via semantic segmentation by using border class
 
 
273-what_is_voronoi
273-what_is_voronoi
 
 
274-object_segmentation_using_voronoi_otsu
274-object_segmentation_using_voronoi_otsu
 
 
275-object_segmentation_and_analysis_using_voronoi_otsu_labeling
275-object_segmentation_and_analysis_using_voronoi_otsu_labeling
 
 
276 - Grain segmentation using less than 10 lines of code in python
276 - Grain segmentation using less than 10 lines of code in python
 
 
277-3D_segmentation_using_voronoi-otsu
277-3D_segmentation_using_voronoi-otsu
 
 
278 - IHC color separation followed by nuclei segmentation using python
278 - IHC color separation followed by nuclei segmentation using python
 
 
285-Object detection using Mask R CNN with XML annotated data
285-Object detection using Mask R CNN with XML annotated data
 
 
286-Object detection using mask RCNN - end to end
286-Object detection using mask RCNN - end to end
 
 
290-Deep Learning based edge detection using HED
290-Deep Learning based edge detection using HED
 
 
291-Object segmentation using Deep Learning based edge detection (HED)​
291-Object segmentation using Deep Learning based edge detection (HED)​
 
 
299-Evaluating sklearn model using KFold cross validation​ in python
299-Evaluating sklearn model using KFold cross validation​ in python
 
 
300-Picking the best model and corresponding hyperparameters using Gridsearch
300-Picking the best model and corresponding hyperparameters using Gridsearch
 
 
301-Evaluating keras model using KFold cross validation
301-Evaluating keras model using KFold cross validation
 
 
302-Tuning deep learning hyperparameters
302-Tuning deep learning hyperparameters
 
 
303 - Reinhard color transformation​
303 - Reinhard color transformation​
 
 
304 - Augmentation of histology images​
304 - Augmentation of histology images​
 
 
306 - Content based image retrieval​ via feature extraction
306 - Content based image retrieval​ via feature extraction
 
 
307 - Segment your images in python without training
307 - Segment your images in python without training
 
 
310-Understanding sub-word tokenization used for NLP
310-Understanding sub-word tokenization used for NLP
 
 
323-Benfords Law
323-Benfords Law
 
 
323-Train a chatbot on your own documents
323-Train a chatbot on your own documents
 
 
324-Chat-based data analysis​-pandasAI
324-Chat-based data analysis​-pandasAI
 
 
332 - All about image annotations​
332 - All about image annotations​
 
 
335 - Converting COCO JSON annotations to labeled mask images
335 - Converting COCO JSON annotations to labeled mask images
 
 
336-Nuclei-Instance-Detectron2.0_YOLOv8_code
336-Nuclei-Instance-Detectron2.0_YOLOv8_code
 
 
337 - Whole Slide Image segmentation for nuclei​ using Detectron2 and YOLOv8
337 - Whole Slide Image segmentation for nuclei​ using Detectron2 and YOLOv8
 
 
340-Comparing Top Large Language Models for Python Code Generation
340-Comparing Top Large Language Models for Python Code Generation
 
 
341-342 - Sholl Analysis
341-342 - Sholl Analysis
 
 
343-344-TMA Core Extraction and Analysis
343-344-TMA Core Extraction and Analysis
 
 
346-3D Sholl Analysis
346-3D Sholl Analysis
 
 
AMT01_extracting_information_from_outlook_emails
AMT01_extracting_information_from_outlook_emails
 
 
AMT_02_extract_gmails_from_a_user
AMT_02_extract_gmails_from_a_user
 
 
Prompt Engineering for Marketers - Tips Tricks 55
Prompt Engineering for Marketers - Tips Tricks 55
 
 
Tips_Tricks_45_white-balance_using_python
Tips_Tricks_45_white-balance_using_python
 
 
images
images
 
 
other_files
other_files
 
 
tips_tricks_36_pyscript_python_in_the_browser
tips_tricks_36_pyscript_python_in_the_browser
 
 
tips_tricks_46-Feature engineering vs feature learning
tips_tricks_46-Feature engineering vs feature learning
 
 
tips_tricks_52_Hidden Gems - defaultdict
tips_tricks_52_Hidden Gems - defaultdict
 
 
tips_tricks_53_Extracting a Targeted Subset from a COCO JSON
tips_tricks_53_Extracting a Targeted Subset from a COCO JSON
 
 
00_A review of COVID19 situation in India using Python.py
00_A review of COVID19 situation in India using Python.py
 
 
017-Reading_Images_in_Python.py
017-Reading_Images_in_Python.py
 
 
018-image_processing_in_pillow.py
018-image_processing_in_pillow.py
 
 
019-image_processing_in_scipy.py
019-image_processing_in_scipy.py
 
 
020-image_processing_in_scikit-image.py
020-image_processing_in_scikit-image.py
 
 
021-scratch_assay_using_scikit_image.py
021-scratch_assay_using_scikit_image.py
 
 
022-denoising.py
022-denoising.py
 
 
023-histogram_segmentation_using_scikit_image.py
023-histogram_segmentation_using_scikit_image.py
 
 
024-random_walker_segmentation_scikit-image.py
024-random_walker_segmentation_scikit-image.py
 
 
025-image_processing_in_openCV_intro1-preprocessing.py
025-image_processing_in_openCV_intro1-preprocessing.py
 
 
026-image_processing_in_openCV_intro1-preprocessing.py
026-image_processing_in_openCV_intro1-preprocessing.py
 
 
027-image_processing_in_openCV_intro2-Thresholding.py
027-image_processing_in_openCV_intro2-Thresholding.py
 
 
028-image_processing_in_openCV_intro2-Thresholding.py
028-image_processing_in_openCV_intro2-Thresholding.py
 
 
029-keypoint detectors and descriptors in opencv.py
029-keypoint detectors and descriptors in opencv.py
 
 
02_Tips_Tricks_python_tips_and_tricks_2_google_image_download.py
02_Tips_Tricks_python_tips_and_tricks_2_google_image_download.py
 
 
030-keypoints_homography_for_registration in opencv.py
030-keypoints_homography_for_registration in opencv.py
 
 
031-reading_and_writing_csv.py
031-reading_and_writing_csv.py
 
 
032-grain_analysis_saving_to_csv.py
032-grain_analysis_saving_to_csv.py
 
 
033-grain_size_analysis_using_wateshed_segmentation.py
033-grain_size_analysis_using_wateshed_segmentation.py
 
 
034a-grain_size_analysis_using_wateshed_segmentation_multiple_files.py
034a-grain_size_analysis_using_wateshed_segmentation_multiple_files.py
 
 
034b-grain_size_analysis_using_wateshed_segmentation_multiple_files_functions.py
034b-grain_size_analysis_using_wateshed_segmentation_multiple_files_functions.py
 
 
035-Cell Nuclei analysis using watershed.py
035-Cell Nuclei analysis using watershed.py
 
 
036-data_analysis_using_Pandas_Intro_data_loading.py
036-data_analysis_using_Pandas_Intro_data_loading.py
 
 

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Author: Dr. Sreenivas Bhattiprolu
Twitter: @digitalsreeni

Supporting code for the tutorials on DigitalSreeni YouTube channel

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

The YouTube channel associated with this code walks you through the entire process of learning to code in Python; all the way from basics to advanced machine learning and deep learning. The primary emphasis will be on image processing and other relevant functionality.

How to cite my work?

1. YouTube video: The general format for citing a YouTube video in APA (American Psychological Association) style is: Author’s Last Name, First Initial. (Year, Month Day Published). Title of video [Video]. YouTube. URL

So, here is an example:

Bhattiprolu, S. (2023, August 23). 330 - Fine tuning Detectron2 for instance segmentation using custom data [Video]. YouTube. https://youtu.be/cEgF0YknpZw

2. GitHub code: There isn't a universally agreed-upon format for citing GitHub repositories, but here's a commonly used one: Author’s Last Name, First Initial. (Year). Title of Repository. GitHub. URL

Example:

Bhattiprolu, S. (2023). python_for_microscopists. GitHub. https://github.com/bnsreenu/python_for_microscopists/blob/master/330_Detectron2_Instance_3D_EM_Platelet.ipynb

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