Saravanan666/pypi-org huggingface dataset:
Package: autodiffpy-free-holmes
autodiffpy- Group 13autodiffpyis a library implementing automatic differentiation to make the calculation of gradients of complex functions fast and precise.Installationpipinstallautodiffpy-free-holmesGetting Started with Forward modefromautodiffpy.forwardimportForwardx=Forward('x',3)f=x**2print(f.value,f.get_gradient('x'))>>>96Getting Started with Reverse modefromautodiffpy.reverseimportReversex=Reverse(3)f=x**2f.gradient_value=1print(f.value,x.get_gradient())>>>96Learn moreTo learn more about the features of forward and reverse mode, including functions of multiple inputs and outputs and elementary functions, head to our documentation atour docs.AuthorsTeddy LiuTapley StephensonZihao XuDavid Zheng
Package: autodiffpypi
team12Team 12's private repository for CS107's final project.DocumentationOur library'sdocumentationInstallationInstall the latest AD library for application usage from PyPi.org with the commandpip install autodiffpypi.Development SetupFor developers interested in contributing to or making changes to the source code, follow the below instructions. Copy the SSH url for the Github repo, then follow the below steps in your terminal.git clone <repo>cd <repo>pip install virtualenvpython -m venv autodiff_venvsource autodiff_venv/bin/activatepip install -r requirements.txtRunning the testsRun the commandpytestto run all test suites. Provide an optional file parameter, e.g. pytest tests/test_dual_number.py to run the specified test.
To view a coverage report, run the following commandpython3 -m pytest --cov=src/AutoDiffPy --cov-fail-under=90.Broader ImpactOur software provides a tool for users to efficiently compute derivatives and jacobian values. As a public package lisenced under the MIT License, this tool may be used in a number of areas ranging from scientific research, personal projects, and industry applications. While our package provides similar functionality to pre-existing AutoDiff software, releasing our own version still contributes to the public domain by providing a unique implementation and framework that others may find particularly suitable to their use case.As the authors of this package, there are a few consequences important to note. Firstly, while our library has undergone unit and integration testing, we cannot guarantee full correctness in all cases, especially under the conditions of large-scale usage. As with most software, this package may be subject to further iteration & fixes. There may be significant consequences if this AutoDiff library is used without user-written tests; therefore we expect any users to take responsibility for ensuring that their applications meet their expectations and that integrating this AutoDiff package does not make them susceptible to incorrect or unexpected results.Software InclusivityThe aim of this package is to increase access to Autodifferentiation capabilities, for students, researchers, programmers, and others who find it applicable. Additionally, our code is released on Github to improve visibility of the internal implementation of such libraries. We welcome open-source contributions or suggestions, which can be submitted by forking this repository and creating a pull-request (this pull request can contain a simple txt file for suggestions). Pull requests will be reviewed and approved by the original authors, who may reach out to you for collaboration. As an international team, we are happy to work with individuals of any background including non-native English speakers, and will provide language accomodation if needed. For those who are visually-impaired, an audio recording of this README can be accessed in the below Google Drive link.
Package: autodigipick
AutoDigipickDigipick puzzles in Bethesda's Starfield are fun to solve, but they can be tedious to solve manually. This script automates the process of solving them.Using OpenCV and Tesseract, the script takes a screenshot of the puzzle, identifies the symbols, and solves the puzzle. It then displays the solution in an extra window which you can display on a second monitor for quick reference.InstallationYou will need to installTesseractand add the path to the tesseract executable to your PATH environment variable.If you use the packed executable release of AutoDigipick, then you can start it by double-clicking the executable.Otherwise, you will need to installPythonand install the dependencies listed in requirements.txt. Then you can run the script withpython autodigipick.py.Alternatively, you can install the package with pip (Recommended in a virtual environment or using tools like pipx):pip install autodigipickand useautodigipick.exeto run the script. The configuration file will be created in the active directory and you can modify it to your needs.UsageOpen autodigipick.toml in a text editor and change the configuration to fit your personal setup.In particular, you will need to change the following values:[capture]monitor_id=3# Change to your monitor's ID[hotkey]solve="F13"# I use macro keys on my keyboard, but you can use any key you wantexit="F14"# it also supports combinations with + like "ctrl+shift+alt+q"[display]resolution=[1920,1080]# Depending on the size of your secondary monitor, you may need to change thisposition=[1000,2300]# Change this to a position on your secondary monitorThen just run the script and press the solve hotkey while the puzzle is on screen.
Package: autodiscjax
AutoDiscJaxAutodiscjaxis a python library built on top ofjaxandequinoxto facilitate automated exploration and simulation of computational models of
biological processes (such as gene, proteins or metabolites networks).
It provides several already-implemented modules and pipelines to organize experimentation on these biological network pathways using
curiosity-driven learning and exploration algorithms.Installationpip install autodiscjaxWhy use AutoDiscJax?AutoDiscJax follows two main design principles:Everything is amodule, where a module is simply a parametrized function that takes inputs and returns outputs (and log_data). All autodiscjax modulesadx.Moduleare implemented as equinox moduleseqx.Module, which essentially allows to represent the function as a callable PyTree (and hence to be compatible with jax transformations) while keeping an intuitive API for model building (python class with a _call_method). The only add-on with respect to equinox is that when instantiating aadx.Module, the user must specify the module's outputs PyTree structure, shape and dtype.An experimentpipelinedefines (i) how modules interact sequentially and exchange information, and (ii) what information should be collected and saved in the experimenthistory.AutoDiscJax provides a handful of already-implement modules and pipelines toSimulate biological networks while intervening on them according to our needsAutomatically organize experimentation in those systems, by implementing a variety of exploration approaches such as random, optimization-driven and curiosity-driven searchAnalyze the discoveries of the exploration method, for instance by testing their robustness to various perturbationsFinally, AutoDiscJax takes advantage of JAX mains features (just-in-time compilation, automatic vectorization and automatic differentation) which are especially advantageous for parallel experimentation and computational speedups, as well as gradient-based optimization.LicenseThe project is licensed under the MIT license.AcknowledgementsAutoDiscJax is inspired by:theauto_disclibrary purpose and structure
(by the FLOWERS team)theequinoxlibrary module definition (by Patrick Kidger)See AlsoLibrary to:sbmltoodejax
Package: autodiscover
Auto DiscoverAuto Discover modules in pythonInstallingInstall withpip:$pipinstallautodiscoverUsagefrompathlibimportPathfromautodiscoverimportAutoDiscoverpath=Path('path/to/module')autodiscover=AutoDiscover(path)autodiscover()With patternfrompathlibimportPathfromautodiscoverimportAutoDiscoverpath=Path('path/to/module')autodiscover=AutoDiscover(path,pattern="models.py")autodiscover()it will import allmodels.pyfrom given pathTests$python-munittest
Package: autodistill-albef
Autodistill ALBEF ModuleThis repository contains the code supporting the ALBEF base model for use withAutodistill.ALBEF, developed by Salesforce, is a computer vision model that supports a range of tasks, including image-text pre-training, image-text retrieval, visual question anserting, and zero-shot classification. You can classify images using ALBEF with Autodistill.Read the fullAutodistill documentation.Read theALBEF Autodistill documentation.InstallationTo use ALBEF with autodistill, you need to install the following dependency:pip3installautodistill-albefQuickstartfromautodistill_albefimportALBEF# define an ontology to map class names to our ALBEF prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=ALBEF(ontology=CaptionOntology({"person":"person","a forklift":"forklift"}))base_model.label("./context_images",extension=".jpeg")LicenseThis project is licensed under a3-Clause BSD license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-altclip
Autodistill AltCLIP ModuleThis repository contains the code supporting the AltCLIP base model for use withAutodistill.AltCLIPis a multi-modal vision model. With AltCLIP, you can compare the similarity between text and images, or the similarlity between two images. AltCLIP was trained on multi-lingual text-image pairs, which means it can be used for zero-shot classification with text prompts in different languages.Read the AltCLIP paper for more information.The Autodistill AltCLIP module enables you to use AltCLIP for zero-shot classification.Read the fullAutodistill documentation.Read theCLIP Autodistill documentation.InstallationTo use AltCLIP with autodistill, you need to install the following dependency:pip3installautodistill-altclipQuickstartfromautodistill_altclipimportAltCLIPfromautodistill.detectionimportCaptionOntology# define an ontology to map class names to our AltCLIP prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated results# then, load the modelbase_model=AltCLIP(ontology=CaptionOntology({"person":"person","a forklift":"forklift"}))results=base_model.predict("construction.jpg")print(results)LicenseThe AltCLIP model is licensed under anApache 2.0 license. See themodel READMEfor more information.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-bioclip
Autodistill BioCLIP ModuleThis repository contains the code supporting the BioCLIP base model for use withAutodistill.BioCLIPis a CLIP model trained on theTreeOfLife-10Mdataset, created by the researchers who made BioCLIP. The dataset on which BioCLIP was trained included more than 450,000 classes.You can use BioCLIP to auto-label natural organisms (i.e. animals, plants) in images for use in training a classification model. You can combine this model with a grounded detection model to identify the exact region in which a given class is present in an image.Learn more about combining models with Autodistill.Read the fullAutodistill documentation.Read theBioCLIP Autodistill documentation.InstallationTo use BioCLIP with autodistill, you need to install the following dependency:pip3installautodistill-bioclipQuickstartfromautodistill_bioclipimportBioCLIP# define an ontology to map class names to our BioCLIP prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelclasses=["arabica","robusta"]base_model=BioCLIP(ontology=CaptionOntology({item:itemforiteminclasses}))results=base_model.predict("../arabica.jpeg")top=results.get_top_k(1)top_class=classes[top[0][0]]print(f"Predicted class:{top_class}")LicenseThis project is licensed under anMIT license.The underlyingBioCLIP modelis also licensed under an MIT license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-blip
Autodistill BLIP ModuleThis repository contains the code supporting the BLIP base model for use withAutodistill.BLIP, developed by Salesforce, is a computer vision model that supports visual question answering and zero-shot classification. Autodistill supports classifying images using BLIP.Read the fullAutodistill documentation.Read theBLIP Autodistill documentation.InstallationTo use BLIP with autodistill, you need to install the following dependency:pip3installautodistill-blipQuickstartfromautodistill_blipimportBLIP# define an ontology to map class names to our BLIP prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=BLIP(ontology=CaptionOntology({"person":"person","a forklift":"forklift"}))base_model.label("./context_images",extension=".jpeg")LicenseThis project is licensed under a3-Clause BSD license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-clip
Autodistill CLIP ModuleThis repository contains the code supporting the CLIP base model for use withAutodistill.CLIP, developed by OpenAI, is a computer vision model trained using pairs of images and text. You can use CLIP with autodistill for image classification.Read the fullAutodistill documentation.Read theCLIP Autodistill documentation.InstallationTo use CLIP with autodistill, you need to install the following dependency:pip3installautodistill-clipQuickstartfromautodistill_clipimportCLIPfromautodistill.detectionimportCaptionOntology# define an ontology to map class names to our CLIP prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=CLIP(ontology=CaptionOntology({"person":"person","a forklift":"forklift"}))results=base_model.predict("./context_images/test.jpg")print(results)base_model.label("./context_images",extension=".jpeg")LicenseThe code in this repository is licensed under anMIT license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-codet
Autodistill CoDet ModuleThis repository contains the code supporting the CoDet base model for use withAutodistill.CoDetis an open vocabulary zero-shot object detection model. The model was described in the "CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection" published by Chuofan Ma, Yi Jiang, Xin Wen, Zehuan Yuan, Xiaojuan Qi. The paper was submitted to NeurIPS2023.Read the fullAutodistill documentation.Read theCoDet Autodistill documentation.InstallationTo use CoDet with autodistill, you need to install the following dependency:pip3installautodistill-codetQuickstartWhen you first run the model, it will download CoDet and its dependencies, as well as the required model configuration and weights. The output during the download process will be verbose. If you stop the download process before it has finished, runrm -rf ~/.cache/autodistill/CoDetbefore running the model again. This ensures that you don't work from a part-installed CoDet setup.When thepredict()function runs, the output will also be verbose. You can ignore the output printed to the console that appears when you callpredict().You can only predict classes in the LVIS vocabulary. You can see a list of supported classes in theclass_names.jsonfile in theautodistill-codet GitHub repository.Use the code snippet below to get started:fromautodistill_codetimportCoDetfromautodistill.detectionimportCaptionOntologyfromautodistill.utilsimportplotimportcv2# define an ontology to map class names to our CoDet prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=CoDet(ontology=CaptionOntology({"person":"person"}))# run inference on an image and display the results# class_names is a list of all classes supported by the model# class_names can be used to turn the class_id values from the model into human-readable class names# class names is defined in self.class_namespredictions=base_model.predict("./context_images/1.jpeg")image=cv2.imread("./context_images/1.jpeg")plot(image=image,detections=predictions,classes=base_model.class_names)# run inference on a folder of images and save the resultsbase_model.label("./context_images",extension=".jpeg")LicenseThis project is licensed under anApache 2.0 license, except where files explicitly note a license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-detic
Autodistill DETIC ModuleThis repository contains the code supporting the DETIC base model for use withAutodistill.DETICis a transformer-based object detection and segmentation model developed by Meta Research.Read the fullAutodistill documentation.Read theDETIC Autodistill documentation.InstallationTo use DETIC with autodistill, you need to install the following dependency:pip3installautodistill-deticQuickstartfromautodistill_deticimportDETIC# define an ontology to map class names to our DETIC prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=DETIC(ontology=CaptionOntology({"person":"person",}))base_model.label("./context_images",extension=".jpg")LicenseThe code in this repository is licensed under anMIT license.See the Meta Research DETIC repository for more information on theDETIC license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-detr
Autodistill DETR ModuleThis repository contains the code supporting the DETR base model for use withAutodistill.DETRis a transformer-based computer vision model you can use for object detection. Autodistill supports training a model using the Meta Research Resnet 50 checkpoint.Read the fullAutodistill documentation.Read theDETR Autodistill documentation.InstallationTo use DETR with autodistill, you need to install the following dependency:pip3installautodistill-detrQuickstartfromautodistill_detrimportDETR# load the modeltarget_model=DETR()# train for 10 epochstarget_model.train("./roads",epochs=10)# run inference on an imagetarget_model.predict("./roads/valid/-3-_jpg.rf.bee113a09b22282980c289842aedfc4a.jpg")LicenseThis project is licensed under anApache 2.0 license. See theHugging Face model card for the DETR Resnet 50model for more information on the model license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-dinov2
Autodistill DINOv2 ModuleThis repository contains the code supporting the DINOv2 base model for use withAutodistill.DINOv2, developed by Meta Research, is a self-supervised training method for computer vision models. This library uses DINOv2 image embeddings with SVM to build a classification model.Read the fullAutodistill documentation.Read theDINOv2 Autodistill documentation.InstallationTo use DINOv2 with autodistill, you need to install the following dependency:pip3installautodistill-dinov2Quickstartfromautodistill_dinov2importDINOv2target_model=DINOv2(None)# train a model# specify the directory where your annotations (in multiclass classification folder format)# DINOv2 embeddings are saved in a file called "embeddings.json" the folder in which you are working# with the structure {filename: embedding}target_model.train("./context_images_labeled")# get class list# print(target_model.ontology.classes())# run inference on the new modelpred=target_model.predict("./context_images_labeled/train/images/dog-7.jpg")print(pred)LicenseThe code in this repository is licensed under aCC Attribution-NonCommercial 4.0 Internationallicense.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-efficientsam
Autodistill EfficientSAM ModuleThis repository contains the code supporting the EfficientSAM base model for use withAutodistill.EfficientSAMis an image segmentation model that was introduced in the paper "EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything". You can use EfficientSAM with autodistill for image segmentation.Read the fullAutodistill documentation.InstallationTo use EfficientSAM with Autodistill, you need to install the following dependency:pip3installautodistill-efficientsamQuickstartThis model returns segmentation masks for all objects in an image.If you want segmentation masks only for specific objects matching a text prompt, we recommend combining EfficientSAM with a zero-shot detection model like GroundingDINO.Read our ComposedDetectionModel documentation for more information about how to combine models like EfficientSAM and GroundingDINO.fromautodistill_efficientsamimportEfficientSAM# define an ontology to map class names to our EfficientSAM prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=EfficientSAM(None)masks=base_model.predict("./image.png")LicenseThis project is licensed under anApache 2.0 license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-efficient-yolo-world
Autodistill EfficientYOLOWorld ModuleThis repository contains the code supporting the EfficientYOLOWorld base model for use withAutodistill.EfficientYOLOWorld is a combination of two models:YOLO-World, a zero-shot object detection model, and;EfficientSAM, an image segmentation model.This model runs EfficientSAM on each bounding box region generated by YOLO-World. This allows you to retrieve both the bounding box and the segmentation mask for each object of interest in an image.Read the fullAutodistill documentation.Read theEfficientYOLOWorld Autodistill documentation.InstallationTo use EfficientYOLOWorld with autodistill, you need to install the following dependency:pip3installautodistill-efficient-yolo-worldQuickstartfromautodistill_efficient_yolo_worldimportEfficientYOLOWorldfromautodistill.detectionimportCaptionOntologyimportcv2importsupervisionassv# define an ontology to map class names to our EfficientYOLOWorld prompt# the ontology dictionary has the format {caption: class}# where caption is the prompt sent to the base model, and class is the label that will# be saved for that caption in the generated annotations# then, load the modelbase_model=EfficientYOLOWorld(ontology=CaptionOntology({"book":"book"}))# predict on an imageresult=base_model.predict("bookshelf.jpeg",confidence=0.1)image=cv2.imread("bookshelf.jpeg")mask_annotator=sv.MaskAnnotator()annotated_frame=mask_annotator.annotate(scene=image.copy(),detections=result,)sv.plot_image(annotated_frame)base_model.label("./context_images",extension=".jpeg")LicenseEfficientSAM is licensed under anApache 2.0 license.YOLO-World is licensed under aGPL-3.0 license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!