Saravanan666/pypi-org huggingface dataset:
Package: deepracer-vehicle-api
Deepracer Vehicle APIdeepracer_vehicle_api package contains methods to communicate with the Deepracer vehicle. These apis are reverse engineering with web  browser to capture network traffic when using the deepracer internal configuration web interface for configuration of the vehicle.APIHigh level methodsshow_vehicle_infomove_forwardmove_backwardturn_rightturn_leftGeneral purpose methodsget_is_usb_connectedget_battery_levelget_raw_video_streamMethods for running autonomous modeset_autonomous_modeset_throttle_percentmethods for running manual modeset_manual_modestart_carstop_carmovemethods for modelsget_modelsget_uploaded_modelsload_modelupload_modelmethods for calibrationset_calibration_modeget_calibration_angleget_calibration_throttleset_calibration_throttleset_calibration_angleInstallationpip install deepracer-vehicle-apiGetting StartedShow vehicle infoimportdeepracer_vehicle_apiclient=deepracer_vehicle_api.Client(password="???",ip="111.222.333.444")client.show_vehicle_info()For more details, see addition examples here:https://github.com/thu2004/deepracer-vehicle-api/tree/master/examples'OthersThis repo has borrowed code and refactored them from :https://github.com/ARCC-RACE/deepracer-rchttps://github.com/gidutz/DeepBullFighting
Package: deep-rec
Factorization Machine models in PyTorchThis package provides a PyTorch implementation of Deep Factorization Machine ;odels and common datasets in Retail Recommendation.Available DatasetsRetail Case Study DataAvailable ModelsModelReferenceDeepFMH Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.xDeepFMJ Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.Environmentconda env create -f environment_conda.yml
source activate environment_condaInstallationpip install deep-recExamplepython main.py --device cpu --epoch 2API Documentationhttps://rixwew.github.io/deep-rec(en construcción)LicenceMIT
Package: deep-replacer
Deep ReplacerGiven a list, set, tuple or dictionary as data input, loop through the data and replace all values that are not a list,
set, tuple or dictionary using a replace function.How to useBasic examplefromdeep_replacerimportDeepReplacerreplacer=DeepReplacer()defmy_replace_func(value:str):"""Return value to upper case"""returnvalue.upper()data=[{"name":"John Doe","hobbies":{"sport":["football","tennis"],"music":["singing","guitar","piano"],},}]data_replaced=replacer.replace(data=data,replace_func=my_replace_func)print(data_replaced)Output:[{"name":"JOHN DOE","hobbies":{"sport":["FOOTBALL","TENNIS"],"music":["SINGING","GUITAR","PIANO"]}}]Example usingkey_depth_rulesargumentfromdeep_replacerimportDeepReplacerfromdeep_replacerimportkey_depth_rulesreplacer=DeepReplacer()defmy_replace_func(value:str):"""Return value to upper case"""returnvalue.upper()data=[{"name":"John Doe","hobbies":{"sport":["football","tennis"],"music":["singing","guitar","piano"],},}]data_replaced=replacer.replace(data=data,replace_func=my_replace_func,key_depth_rules={"hobbies:sport":[key_depth_rules.IGNORE]},# Ignore key at depth 'hobbies:sport')print(data_replaced)Output:[{"name":"JOHN DOE","hobbies":{"sport":["football","tennis"],"music":["SINGING","GUITAR","PIANO"]}}]
Package: deeprhythm
DeepRhythm: High-Speed Tempo PredictionIntroductionDeepRhythm is a Convolutional Neural Network (CNN) designed for rapid, precise tempo prediction, specifically on modern music.The implementation is heavily inspired by [1].Audio is batch-processed using a vectorized HCQM, drastically reducing computation time by avoiding the common bottlenecks encountered in feature extraction.BenchmarksMethodAcc1 (%)Acc2 (%)Avg. Time (s)Total Time (s)DeepRhythm (cuda)95.9196.540.02120.11DeepRhythm (cpu)95.9196.540.12115.02TempoCNN (cnn)84.7897.691.211150.43TempoCNN (fcn)83.5396.541.191131.51Essentia (multifeature)87.9397.482.722595.64Essentia (percival)85.8395.071.351289.62Essentia (degara)86.4697.171.381310.69Librosa66.8475.130.48460.52Test done on 953 songs, mostly Electronic, Hip Hop, Pop, and RockAcc1 = Prediction within +/- 2% of actual bpmAcc2 = Prediction within +/- 2% of actual bpm or a multiple (e.g. 120 ~= 60)Timed from filepath in to bpm out (audio loading, feature extraction, model inference)I could only get TempoCNN to run on cpu (it requires Cuda 10 and I'm not downgrading my Cuda install for curiosity's sake)InstallationTo install DeepRhythm, ensure you have Python and pip installed. Then run:pipinstalldeeprhythmUsageTo predict the tempo of a song with DeepRhythm:fromdeeprhythmimportDeepRhythmPredictormodel=DeepRhythmPredictor()tempo=model.predict('path/to/song.mp3')print(f"Predicted Tempo:{tempo}BPM")References[1] Hadrien Foroughmand and Geoffroy Peeters, “Deep-Rhythm for Global Tempo Estimation in Music”, in Proceedings of the 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, Nov. 2019, pp. 636–643. doi: 10.5281/zenodo.3527890.[2] K. W. Cheuk, H. Anderson, K. Agres and D. Herremans, "nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 161981-162003, 2020, doi: 10.1109/ACCESS.2020.3019084.
Package: deeproc
deeprocdeeprocis a Python package written by André Carrington and Yusuf Sheikh, for in-depth analysis of ROC plots in multiple groups/regions or a single region of interest.  It provides measures and plots which measure in a group: AUC (concordant partial AUC), the C statistic (partial C statistic), average sensitivity (partial AUC), average specificity (horizontal partial AUC) and other pre-test and post-test measures including average precision and inverse precision, also called average positive and negative predictive value.Groups are contiguous regions of predicted risk or probability (e.g., ranges of FPR or TPR) or non-contiguous demographic groups or clinical groups/arms.  Groups may be overlapping and not cover the whole ROC curve, or they may be mutually exclusive and perfectly cover the ROC curve.FeaturesClasses:SimpleROCFullROCDeepROCConcordanceMatrixPlotInstallationThe package can be installed from PyPi:pipinstalldeeproc
Package: deepse
DeepSEDeepSE: 面向生产环境的Sentence Embedding！目录安装实现的模型2.1BERT和RoBERTa2.2SimCSE安装克隆仓库:gitclonehttps://github.com/luozhouyang/deepse.git或者从pypi安装:pipinstall-Udeepse实现的模型目前支持的模型如下：原始的BERT和RoBERTaSimCSEUnsupervised SimCSESupervised SimCSESupervised SimCSE (with hard negative)BERT和RoBERTaTODO: 补充文档SimCSE对于不同的版本，训练数据的格式稍有不同，但是都是普通文本文件，每一行都是一个JSON格式的训练样本。对于Unsupervised SimCSE，每个样本都需要含有sequence字段。举例如下：{"sequence":"我很讨厌自然语言处理"}{"sequence":"我对自然语言处理很感兴趣"}对于Supervised SimCSE，每个样本都需要包含sequence和positive_sequence字段。举例如下：{"sequence":"我很讨厌自然语言处理","positive_sequence":"我不喜欢自然语言处理"}{"sequence":"我对自然语言处理很感兴趣","positive_sequence":"我想了解自然语言处理"}对于Supervised SimCSE with hard negative，每个样本都需要包含sequence、positive_sequence和negative_sequence字段。如果positive_sequence字段为空，则会自动使用sequence作为自己的positive_sequence。举例如下：{"sequence":"我很讨厌自然语言处理","positive_sequence":"我不喜欢自然语言处理","negative_sequence":"我想了解自然语言处理"}{"sequence":"我对自然语言处理很感兴趣","positive_sequence":"我想了解自然语言处理","negative_sequence":"我很讨厌自然语言处理"}然后，使用以下命令即可训练：exportPRETRAINED_MODEL_PATH=/path/to/your/pretrained/bert/dirnohuppythonrun_simcse.py>>log/run_simcse.log2>&1&tail-flog/run_simcse.log参数可以到run_simcse.py直接修改。模型会同时保存成Checkpoint格式和SavedModel格式，后者你可以直接用tensorflow/serving部署在生产环境。
Package: deepsea
DeepSeaThis work presents a versatile and trainable deep-learning-based software, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images.DatasetsTo download our datasets go tohttps://deepseas.org/datasets/or:Link toOriginal annotated datasetLink todataset example for cell segmentationLink todataset example for cell trackingUsageExample of single cell image segmentationfrom deepsea.test_single_image_segmentation import apply_img_segmentation
import cv2
import os

output_dir='test_results/'
img = cv2.imread("segmentation_dataset/test/images/A11_z016_c001.png",0)
label_img,binary_mask,overlay_img,img=apply_img_segmentation(img)
cv2.imwrite(os.path.join(output_dir, 'label_img.png'), label_img)
cv2.imwrite(os.path.join(output_dir, 'binary_mask.png'), binary_mask)
cv2.imwrite(os.path.join(output_dir, 'overlay_img.png'), overlay_img)
cv2.imwrite(os.path.join(output_dir, 'original_img_resized.png'), img)Example of tracking the single set of cell image sequencesfrom deepsea.test_single_set_tracking import apply_cell_tracking
import cv2
import os

single_image_set_dir="tracking_dataset/test/set_13_MESC/images/"
output_dir='test_results/'
img_list=[]
for img_name in sorted(os.listdir(single_image_set_dir)):
    img_list.append(cv2.imread(os.path.join(single_image_set_dir,img_name),0))

cell_labels,cell_centroids,tracked_imgs=apply_cell_tracking(img_list)
if tracked_imgs:
    for id, img in enumerate(tracked_imgs):
        cv2.imwrite(os.path.join(output_dir, 'img_{:04d}.png'.format(id)), img)DeepSea GUI SoftwareOur DeepSea software is available onhttps://deepseas.org/software/with examples and instructions. DeepSea software is a user-friendly and automated software designed
to enable researchers to 1) load and explore their phase-contrast cell images in a
high-contrast display, 2) detect and localize cell bodies using the pre-trained DeepSea
segmentation model, 3) track and label cell lineages across the frame sequences using the pre-trained
DeepSea tracking model, 4) manually correct the DeepSea models' outputs using user-friendly editing
options, 5) train a new model with a new cell type dataset if needed, 6) save the results and cell label
and feature reports on the local system. It employs our latest trained DeepSea models in the segmentation and tracking processes.
It employs our last trained DeepSea models in the segmentation and tracking processes.Useful InformationIf you have any questions, contact us atabzargar@ucsc.edu.
Package: deepsearch
deepsearchis aHaystackextension used to
index deep and nested model relationships. Its key features are:deepsearch.indexes.DeepSearchIndexclass able to
index deeply nested related objects and their fields.deepsearch.models.FieldBoostmodel that stores weights of
each index field for query-time field boosting.resume_indexcommand able to reindex a slice of all
objects or only a subset of index fields.Celerysupport for real-time indexing.InstallationRequirements:Python >= 2.6Django >= 1.4django-haystack >= 2.1.0South.>= 0.8.1celery>=3.1(optional)Recommended:haystackbrowser(to inspect indexed data)Setup:Include'deepsearch'in yourINSTALLED_APPS.ConfigureHAYSTACK_CONNECTIONS.To enable real-time indexing add the following line tosettings.py:HAYSTACK_SIGNAL_PROCESSOR='deepsearch.signals.DeepSearchSignalProcessor'Createsearch_indexes.pyin your app directory.python manage.py init_booststo update index field weight values. You
should run this whenever you modify index schema.python manage.py rebuild_indexwill update index anddeepsearch.models.IndexRelationtable.
Package: deepsearchai
A python lib to search for relevant media from your data corpus. This library not only helps you search the relevant media but also highlights the most relevant section of the media. For example, given a list of audio files you can search over them to find the exact second of the file that is relevant to your search query.Users can upload images, text, audios or videos to add as data to be searchedLLM models (customisable) help tag, describe and index the dataEmbeddings from this are then added to a vector database (configurable)Search APIs that help relevant data (across all multimedia types) from the vector database.Instructions to run the UINavigate to the root folder of the repopoetry shell && poetry lock && poetry installstreamlit run deepsearchai/streamlit_app.pyAlternately, you can runfrom deepsearch.app import App
app = App()

app.run()Instructions to add local data from the CLIOpen Python Shell and run the following commandsfrom deepsearch.app import App
app = App()

app.add_data(<LOCAL_PATH>)Instructions to add S3 data from the CLIOpen Python Shell and run the following commandsfrom deepsearch.app import App
app = App()

app.add_data(<S3_PATH>)Audio UsageHaving ffmpeg installed is a prerequisite. All major package installers include ffmpeg,
but if you need to manually install it, seehere. You may
referhttps://github.com/openai/whisper/blob/main/README.mdYou can only have 1 model per datatype. For example: Clip for images and Whisper for Audio.
Only one vector_database. Embedding from each datatype will go into a new collection.The collection names would default to audio_collection, image_collection but can be overriden with a database config file.Video UsageWe currently support loading a youtube channelA youtube channel ID can be obtained fromhttps://www.youtube.com/account_advancedFor a channel you dont own, the channel ID is the last part of the URL. For example, the channel ID forhttps://www.youtube.com/channel/UCXuqSBlHAE6Xw-yeJA0Tunwis UCXuqSBlHAE6Xw-yeJA0TunwSetting up Keys for VideoSet 'GOOGLE_CLIENT_API_KEY' environment variable.Pytorch issuesInstall pytorch via pip causes issues for Mac M1/M2 machines. For such cases, you might have to install pytorch via conda. Please referhttps://www.geeksforgeeks.org/how-to-install-pytorch-on-macos/Triton IssuesInstalling triton can be tricky with Mac M1. This happens when attempting to install the audio dependency for the package. Please remote triton manually from the poetry.lock file if the issue persists.CitationIf you utilize this repository, please consider citing it with:@misc{deepsearch,
  author = {Rupesh Bansal, Shiwangi Shah},
  title = {Deepsearch: Semantic search on multimedia sources like audio, video and images},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/deepsearch-ai/deepsearch}},
}LicenseThis project is licensed under the Apache License, Version 2.0. Please see the LICENSE: LICENSE file for more information.
Package: deepsearchkit
Note: This project is still a work-in-progress. It works, but it doesn't have many features yet. Please suggest features, report problems (bugs should go in Issues), or just say hi in Discussions!:mag: DeepSearchKitA small, fast, and easy package that allows easy semantic searching using :brain: artificial intelligence. It is based on and similar to the closed-source DeepSearch system.:computer: InstallationYou can install the latest stable version from the registry:pip3 install deepsearchkitYou can install the very latest version directly from the Git repository, however certain features may not work:pip3 install git+https://github.com/fakerybakery/deepsearchkit:white_check_mark: FeaturesCPU, CUDA, and MPS support (enhanced GPU acceleration)!Simple usage:newspaper: UsageDocumentation is availablehere.:thought_balloon: TodoIntegrate DeepSearch into DeepSearchKitOpen-source DeepSearchAdd Web Interface (from DeepSearch)Add document search demoAdd document chat demoAdd upsert feature (txtai#251)Add more data support, e.g. parquet, MySQL/hosted DBsCustom prompt/data format for multiple columns in JSONCustom progress callback for indexingMake some example projectsChat with a folder using open-sourced conversational modelsSearch an entire directoryAllow easy publishing with a.dskpkgfile - compressed DeepSearchKit package that includes the index, the data, and some attributes (name, author, license, etc)Maybe in the future: "DSK Hub" - hub for DSK packages:memo: CreditsWe would like to thank the authors of the following open source projects:sentence-transformerstxtai:briefcase: Disclaimer/Agreement (Read BEFORE you contribute!)In every new issue/PR, make sure to include "I agree to the disclaimer!"By using/contributing to this software, you agree to theagreement.
Package: deepsearch-toolkit
Deep Search ToolkitInteract with the Deep Search platform for new knowledge explorations and discoveriesThe Deep Search Toolkit is a Python SDK and CLI allowing users to interact with theDeep Searchplatform.
The Toolkit provides easy-to-use features for several common document workflows such as conversion, graph creation, and querying, including semantic retrieval and RAG.Quick linksDeep SearchDeep Search ToolkitDeep Search ExamplesInstallpipinstalldeepsearch-toolkitGetting startedCheck outGetting started.Get help and supportPlease feel free to connect with us using thediscussion section.ContributingPlease readContributing to Deep Search Toolkitfor details.ReferencesIf you useDeep Searchin your projects, please consider citing the following:@software{DeepSearchToolkit,author={DeepSearchTeam},month = {6},title = {{Deep Search Toolkit}},url = {https://github.com/DS4SD/deepsearch-toolkit},version = {main},year = {2022}}LicenseTheDeep Search Toolkitcodebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.
Package: deepsegment
DeepSegment: A sentence segmenter that actually works!Note: For the original implementation please use the "master" branch of this repo.The Demo for deepsegment (en) + deeppunct is available athttp://bpraneeth.com/projects/deeppunctCode documentation available athttp://bpraneeth.com/docsInstallation:pipinstall--upgradedeepsegmentSupported languages:en - english (Trained on data from various sources)fr - french (Only Tatoeba data)it - italian (Only Tatoeba data)Usage:fromdeepsegmentimportDeepSegment# The default language is 'en'segmenter=DeepSegment('en')segmenter.segment('I am Batman i live in gotham')# ['I am Batman', 'i live in gotham']Using with tf serving docker imagedockerpullbedapudi6788/deepsegment_en:v2
dockerrun-d-p8500:8500bedapudi6788/deepsegment_en:v2fromdeepsegmentimportDeepSegment# The default language is 'en'segmenter=DeepSegment('en',tf_serving=True)segmenter.segment('I am Batman i live in gotham')# ['I am Batman', 'i live in gotham']Finetuning DeepSegmentSince one-size will never fit all, finetuning deepsegment's default models with your own data is encouraged.fromdeepsegmentimportfinetune,generate_datax,y=generate_data(['my name','is batman','who are','you'],n_examples=10000)vx,vy=generate_data(['my name','is batman'])# NOTE: name, epochs, batch_size, lr are optional arguments.finetune('en',x,y,vx,vy,name='finetuned_model_name',epochs=number_of_epochs,batch_size=batch_size,lr=learning_rate)Using with a finetuned checkpointfromdeepsegmentimportDeepSegmentsegmenter=DeepSegment('en',checkpoint_name='finetuned_model_name')Training deepsegment on custom data:https://colab.research.google.com/drive/1CjYbdbDHX1UmIyvn7nDW2ClQPnnNeA_mSimilar Projects:https://github.com/bminixhofer/nnsplit(with bindings for Python, Rust and Javascript.)
Package: deepsemhist
Overviewdeep_semantic_representation is a python library to apply tissue/cell segmentation models for histology images (bioRxiv, 2022).
Fucntions for plotting the distribution are also implemented.InstallationThe package can be installed withpip:$pipinstalldeepsemanticPrerequisitesPython version 3.6 or newer.numpy >=1.20.3joblib >=0.13.2Pillow >=8.0.1nmslib >=2.0.6matplotlib >= 3.5.0scikit-learn >=1.1.0seaborn >=0.10.1pandas >=1.1.0cv2Recommended EnvironmentOSLinuxMacLicenseThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC-BY-NC-SA 4.0)For non-commercial use, please use the code under CC-BY-NC-SA.If you would like to use the code for commercial purposes, please contact us <ishum-prm@m.u-tokyo.ac.jp>.CitationIf you use this library for your research, please cite:Komura, D., Kawabe, A., Fukuta, K., Sano, K., Umezaki, T., Koda, H., Suzuki, R., Tominaga, K., Ochi, M., Konishi, H., Masakado, F., Saito, N., Sato, Y., Onoyama, T., Nishida, S., Furuya, G., Katoh, H., Yamashita, H., Kakimi, K., Seto, Y., Ushiku, T., Fukayama, M., Ishikawa, S.,“Universal encoding of pan-cancer histology by deep texture representations.”Cell Reports 38, 110424,2022.https://doi.org/10.1016/j.celrep.2022.110424
Package: deepseqcoverageqc
DeepSeqCoverageQCCompute coverage QC metrics for deep targeted sequencing dataExplore the docs »Request Feature|Report Bug⭐ Consider starring the repo! ⭐Quick StartUsingDockerdockerrun-itpskolekar/deepseqcoverageqc:0.3.3DeepSeqCoverageQC--helpUsingMinicondacondacreate--nameDeepSeqCoverageQCpython==3.9
condaactivateDeepSeqCoverageQC
python-mpipinstalldeepseqcoverageqc
DeepSeqCoverageQC--helpUsing PipNoteRequiresPythonversion >=3.9pipinstalldeepseqcoverageqcUsagePlease refer to thedetailed documentationhereDeepSeqCoverageQCis a command line interface (CLI) app with the following commands$DeepSeqCoverageQC--help

Usage:DeepSeqCoverageQC[OPTIONS]COMMAND[ARGS]...ComputecoverageQCfortargeteddeepsequencingdata╭─Options─────────────────────────────────────────────────────────────────────────╮
│--helpShowthismessageandexit.│
╰───────────────────────────────────────────────────────────────────────────────────╯
╭─Commands────────────────────────────────────────────────────────────────────────╮
│buildIndexGenerateindexforthepanel│
│computeCoverageQCComputecoverageQCmetricsacrosspanelregions.│
╰───────────────────────────────────────────────────────────────────────────────────╯Build panel index$DeepSeqCoverageQCbuildIndex--help

Generateindexforthepanel╭─Options─────────────────────────────────────────────────────────────────────────╮
│*--panelFilePATHFilewithpanelregions.Fileshouldhavefour│
│tab-delimitedcolumns:Chr,Start,End,Gene│
│[required]│
│--outfilePATHOutputfilename.│
│--paddingINTEGERPadding(bp)forregions.│
│--chr/--no-chrAdd'chr'prefix│
│--helpShowthismessageandexit.│
╰───────────────────────────────────────────────────────────────────────────────────╯Compute region and sample level QC metrics$DeepSeqCoverageQCcomputeCoverageQC--help

Usage:DeepSeqCoverageQCcomputeCoverageQC[OPTIONS]ComputecoverageQCmetricsacrosspanelregions.╭─Options─────────────────────────────────────────────────────────────────────────╮
│*--panelPosFilePATHFilewithpanelpositionsasgenerated│
│bybuildIndexcommand.Fileshouldhave│
│sixcolumns:Chr.Pos,Chr,Start,End,│
│Gene,RegionLength│
│[required]│
│*--summaryPosFilePATHFilewithuniquepositionsinthepanel│
│tocomputesamplelevelsummary│
│statistics.Fileshouldhaveonecolumn:│
│Chr.Pos,asgeneratedbybuildIndex│
│command.│
│[required]│
│*--countFilePATHCountfile(s)generatedbySequencErr│
│program.File(s)should'veseven│
│columns:Chr,Pos,A_Q_30,C_Q_30,│
│G_Q_30,T_Q_30,N_Q_30│
│[required]│
│--outdirPATHOutputdirectorypath.│
│--outSummary/--no-outSummaryOutputcountsatsummarypositions│
│--helpShowthismessageandexit.│
╰───────────────────────────────────────────────────────────────────────────────────╯PublicationManuscript under preparationContactDr. Pandurang KolekarDr. Xiaotu MaCOPYRIGHTCopyright © 2023 Pandurang Kolekar, St. Jude Children's Research Hospital