Saravanan666/pypi-org huggingface dataset:
Package: autodistill-remote-clip
Autodistill RemoteCLIP ModuleThis repository contains the code supporting the RemoteCLIP base model for use withAutodistill.RemoteCLIPis a vision-language CLIP model trained on remote sensing data. According to the RemoteCLIP README:RemoteCLIP outperforms previous SoTA by 9.14% mean recall on the RSICD dataset and by 8.92% on RSICD dataset. For zero-shot classification, our RemoteCLIP outperforms the CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets.Read the fullAutodistill documentation.Read theRemoteCLIP Autodistill documentation.InstallationTo use RemoteCLIP with autodistill, you need to install the following dependency:pip3installautodistill-remote-clipQuickstartfromautodistill_remote_clipimportRemoteCLIPfromautodistill.detectionimportCaptionOntology# define an ontology to map class names to our RemoteCLIP 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=RemoteCLIP(ontology=CaptionOntology({"airport runway":"runway","countryside":"countryside",}))predictions=base_model.predict("runway.jpg")print(predictions)LicenseThis Autodistill module is licensed under an MIT license. At the time of publishing this project, the RemoteCLIP model and weights had no attached license. Refer to theRemoteCLIP repositoryfor the most up-to-date licensing information regarding the model.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-roboflow-universe
Autodistill Roboflow Universe ModuleThis repository contains the code supporting the Roboflow Universe base model for use withAutodistill.Roboflow Universeis a community where people share computer vision models and datasets. Over 50,000 models and 250,000 datasets have been shared on Universe, with new models available every day. You can use Autodistill to run object detection, classification, and segmentation models hosted on Roboflow Universe.[!NOTE]
Using this project will use Roboflow API calls. You will need a free Roboflow account to use this project.Sign up for a free Roboflow accountto get started.Learn more about pricing.Read the fullAutodistill documentation.Read theRoboflow Universe Autodistill documentation.InstallationTo use models hosted on Roboflow Universe with autodistill, you need to install the following dependency:pip3installautodistill-roboflow-universeQuickstart[!NOTE]
Autodistill uses ontology to map model predictions to the expected class labels. For other Autodistill models, the term 'caption' is used when the model accepts prompting or a description for a prediction. When using Roboflow Universe as an Autodistill base model, the 'caption' will be the class name/label that the Universe model will return.fromautodistill_roboflow_universeimportRoboflowUniverseModelfromautodistill.detectionimportCaptionOntologyfromautodistill.utilsimportplotimportcv2# define an ontology to map class names to our Roboflow model 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 in the generated annotationsmodel_configs=[("PROJECT_ID",VERSION_NUMBER)]base_model=RoboflowUniverseModel(ontology=CaptionOntology({"person":"person","forklift":"vehicle"}),api_key="ROBOFLOW_API_KEY",model_configs=model_configs,)# run inference on a single imageresult=base_model.predict("image.jpeg")print(result)plot(image=cv2.imread("image.jpeg"),detections=result,classes=base_model.ontology.classes(),)# label a folder of imagesbase_model.label("./context_images",extension=".jpeg")Above, replace:API_KEY: with your Roboflow API keyPROJECT_NAME: with your Roboflow project ID.VERSION: with your Roboflow model version.model_type: with the type of model you want to run. Options areobject-detection,classification, orsegmentation. This value must be the same as the model type trained on Roboflow Universe.You can run multiple models on a single image. This is ideal if you need to identify multiple objects using different models hosted on Roboflow Universe. To run multiple models, add the models you want to run in themodel_configslist. For example:model_configs=[("PROJECT_ID",VERSION_NUMBER),("PROJECT_ID",VERSION_NUMBER)]All models will be run on every image.Learn how to retrieve your Roboflow API key.Learn how to retrieve a model ID.LicenseThis project 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-sam-hq
Autodistill Segment Anything HQ ModuleThis repository contains the code supporting the Segment Anything base model for use withAutodistill.SAM HQis a zero-shot segmentation model capable of producing detailed masks, developed byETH VIS. SAM HQ can segment an entire image into masks, or use points to segment specific parts of an object. You can use Segment Anything with Autodistill to segment objects. Segment Anything does not assign classes, so you should use SAM HQ model with a tool like Grounding DINO or GPT-4V.Read the fullAutodistill documentation.Read theSAM HQ Autodistill documentation.InstallationTo use SAM HQ with autodistill, you need to install the following dependency:pip3installautodistill-sam-hqQuickstartfromautodistill_sam_hqimportHQSAMbase_model=HQSAM(None)masks=base_model.predict("./image.jpeg")print(masks)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-seggpt
Autodistill SegGPT ModuleThis repository contains the code supporting the SegGPT base model for use withAutodistill.SegGPTis a transformer-based, few-shot semantic segmentation model developed byBAAI Vision.This model performs well on task-specific segmentation tasks when given a few labeled images from which to learn features about the objects you want to identify.Read the fullAutodistill documentation.Read theSegGPT Autodistill documentation.InstallationTo use SegGPT with Autodistill, you need to install the following dependency:pip3installautodistill-seggptAbout SegGPTSegGPT performs "in-context" segmentation. This means it requires a handful of pre-labelled "context" images.You will need some labeled images to use SegGPT. Don't have any labeled images? Check outRoboflow Annotate, a feature-rich annotation tool from which you can export data for use with Autodistill.Quickstartfromautodistill_seggptimportSegGPT,FewShotOntologybase_model=SegGPT(ontology=FewShotOntology(supervision_dataset))base_model.label("./unlabelled-photos",extension=".jpg")How to load data from RoboflowLabelling and importing images is easy!You can useRoboflow Annotateto label a few images (1-3 should work fine). For your Project Type, make sure to pick Instance Segmentation, as you will be labelling with polygons.Once you have labelled your images, you can press Generate > Generate New Version. You can use all the default options--no Augmentations are necessary.Once your dataset version is generated, you can press Export > Continue.Then you will get some download code to copy. It should look something like this:!pipinstallroboflowfromroboflowimportRoboflowrf=Roboflow(api_key="ABCDEFG")project=rf.workspace("lorem-ipsum").project("dolor-sit-amet")dataset=project.version(1).download("yolov8")Note: if you are not using a notebook environment, you should remove!pip install roboflowfrom your code, and runpip install roboflowin your terminal instead.To import your dataset into Autodistill, run the following:importsupervisionassvsupervision_dataset=sv.DetectionDataset.from_yolo(images_directory_path=f"{dataset.location}/train/images",annotations_directory_path=f"{dataset.location}/train/labels",data_yaml_path=f"{dataset.location}/data.yaml",force_masks=True)LicenseThe code in this repository is licensed under anMIT license.See the SegGPT repository for more information on theSegGPT license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-segment-anything
Autodistill Segment Anything ModuleThis repository contains the code supporting the Segment Anything base model for use withAutodistill.Segment Anything (SAM)is a segmentation model developed by Meta AI Research. Segment Anything can segment an entire image into masks, or use points to segment specific parts of an object. You can use Segment Anything with Autodistill to segment objects. Segment Anything does not assign classes, so you should use the model with a tool like Grounding DINO or GPT-4V.Read the fullAutodistill documentation.Read theSAM Autodistill documentation.InstallationTo use SAM with autodistill, you need to install the following dependency:pip3installautodistill-segment-anythingQuickstartfromautodistill_segment_anythingimportSegmentAnythingbase_model=SegmentAnything(None)masks=base_model.predict("./image.jpeg")print(masks)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-siglip
Autodistill SigLIP ModuleThis repository contains the code supporting the SigLIP 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 theSigLIP Autodistill documentation.InstallationTo use SigLIP with autodistill, you need to install the following dependency:pip3installautodistill-clipQuickstartfromautodistill_siglipimportSigLIPfromautodistill.detectionimportCaptionOntology# define an ontology to map class names to our SigLIP 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 modellabels=["person","a forklift"]base_model=SigLIP(ontology=CaptionOntology({item:itemforiteminlabels}))results=base_model.predict("image.jpeg",confidence=0.1)top_1=results.get_top_k(1)# show top labelprint(labels[top_1[0][0]])# label folder of imagesbase_model.label("./context_images",extension=".jpeg")LicenseThe SigLIP model 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-swinv2
Autodistill autodistill-swinv2The autodistill-swinv2 package integrates with theAutodistill ecosystem, a framework for distilling large models into smaller, edge-ready models.If you are interested in implementing the autodistill-swinv2 package for use with Autodistill, leave an issue on the mainAutodistill GitHub repositoryto discuss getting started.
Package: autodistill-transformers
Autodistill Transformers ModuleThis repository contains the code supporting the Transformers models model for use withAutodistill.Transformers, maintained by Hugging Face, features a range of state of the art models for Natural Language Processing (NLP), computer vision, and more.This package allows you to write a function that calls a Transformers object detection model and use it to automatically label data. You can use this data to train a fine-tuned model using an architecture supported by Autodistill (i.e.YOLOv8,YOLOv5, orDETR).Read the fullAutodistill documentation.InstallationTo use Transformers with autodistill, you need to install the following dependency:pip3installautodistill-transformersQuickstartThe following example shows how to use the Transformers module to label images using theOwlv2ForObjectDetectionmodel.You can update theinference()functon to use any object detection model supported in the Transformers library.importcv2importtorchfromautodistill.detectionimportCaptionOntologyfromautodistill.utilsimportplotfromtransformersimportOwlViTForObjectDetection,OwlViTProcessorfromautodistill_transformersimportTransformersModelprocessor=OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")model=OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")definference(image,prompts):inputs=processor(text=prompts,images=image,return_tensors="pt")outputs=model(**inputs)target_sizes=torch.Tensor([image.size[::-1]])results=processor.post_process_object_detection(outputs=outputs,target_sizes=target_sizes,threshold=0.1)[0]returnresultsbase_model=TransformersModel(ontology=CaptionOntology({"a photo of a person":"person","a photo of a cat":"cat",}),callback=inference,)# run inferenceresults=base_model.predict("image.jpg",confidence=0.1)print(results)# plot resultsplot(image=cv2.imread("image.jpg"),detections=results,classes=base_model.ontology.classes(),)# label a directory of imagesbase_model.label("./context_images",extension=".jpeg")LicenseThis project 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-vit
Autodistill ViT ModuleThis repository contains the code supporting the ViT target model for use withAutodistill.ViTis a classification model pre-trained on ImageNet-21k, developed by Google. You can train ViT classification models using Autodistill.Read the fullAutodistill documentation.Read theViT Autodistill documentation.InstallationTo use the ViT target model, you will need to install the following dependency:pip3installautodistill-vitQuickstartfromautodistill_vitimportViTtarget_model=ViT()# train a model from a classification folder structuretarget_model.train("./context_images_labeled/",epochs=200)# run inference on the new modelpred=target_model.predict("./context_images_labeled/train/images/dog-7.jpg",conf=0.01)LicenseThe code in this repository 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-vlpart
Autodistill VLPart ModuleThis repository contains the code supporting the VLPart base model for use withAutodistill.VLPart, developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary.autodistill-vlpartenables you to use VLPart to auto-label images for use in training a fine-tuned model.autodistill-vlpartsupports the LVIS vocabulary.Read the fullAutodistill documentation.Read theVLPart Autodistill documentation.InstallationTo use VLPart with autodistill, you need to install the following dependency:pip3installautodistill-vlpartQuickstartfromautodistill_vlpartimportVLPartfromautodistill.detectionimportCaptionOntologyfromautodistill.utilsimportplot# define an ontology to map class names to our VLPart 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=VLPart(ontology=CaptionOntology({"person":"person"}))predictions=base_model.predict("./image.png")print(predictions)plot(image=cv2.imread("./image.png"),classes=base_model.class_names,detections=predictions)# label the images in the context_images folderbase_model.label("./context_images",extension=".jpeg")LicenseThis project 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-yolonas
Autodistill YOLO-NAS ModuleThis repository contains the code supporting the YOLO-NAS target model for use withAutodistill.YOLO-NASis an object detection model developed byDeci AI.You can useautodistillto train a YOLO-NAS object detection model on a dataset of labelled images generated by the base models thatautodistillsupports.Read the fullAutodistill documentation.Read theYOLO-NAS Autodistill documentation.InstallationTo use the YOLOv5 target model, you will need to install the following dependency:pip3installautodistill-yolo-nasQuickstartfromautodistill_yolo_nasimportYOLONAStarget_model=YOLONAS("YOLOv5n.pt")# train a model# specify the directory where your annotations (in YOLO format) are storedtarget_model.train("./context_images_labeled",epochs=20)# run inference on the new modelpred=target_model.predict("./context_images_labeled/train/images/dog-7.jpg",confidence=0.01)LicenseThe YOLO-NAS model is licensed under theYOLO-NAS License.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-yolov4
Autodistill autodistill-yolov4The autodistill-yolov4 package integrates with theAutodistill ecosystem, a framework for distilling large models into smaller, edge-ready models.If you are interested in implementing the autodistill-yolov4 package for use with Autodistill, leave an issue on the mainAutodistill GitHub repositoryto discuss getting started.
Package: autodistill-yolov5
Autodistill YOLOv5 ModuleThis repository contains the code supporting the YOLOv5 target model for use withAutodistill.YOLOv5is an open-source computer vision model by Ultralytics, the creators of YOLOv5. You can useautodistillto train a YOLOv5 object detection model on a dataset of labelled images generated by the base models thatautodistillsupports.View ourYOLOv5 Instance Segmentationpage for information on how to train instance segmentation models.Read the fullAutodistill documentation.Read theYOLOv5 Autodistill documentation.InstallationTo use the YOLOv5 target model, you will need to install the following dependency:pip3installautodistill-yolov5Quickstartfromautodistill_YOLOv5importYOLOv5target_model=YOLOv5("YOLOv5n.pt")# train a modeltarget_model.train("./context_images_labeled/data.yaml",epochs=200)# run inference on the new modelpred=target_model.predict("./context_images_labeled/train/images/dog-7.jpg",conf=0.01)LicenseThe code in this repository is licensed under anAGPL 3.0 license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodistill-yolov6
Autodistill autodistill-yolov6The autodistill-yolov6 package integrates with theAutodistill ecosystem, a framework for distilling large models into smaller, edge-ready models.If you are interested in implementing the autodistill-yolov6 package for use with Autodistill, leave an issue on the mainAutodistill GitHub repositoryto discuss getting started.
Package: autodistill-yolov9
Autodistill autodistill-yolov9The autodistill-yolov9 package integrates with theAutodistill ecosystem, a framework for distilling large models into smaller, edge-ready models.If you are interested in implementing the autodistill-yolov9 package for use with Autodistill, leave an issue on the mainAutodistill GitHub repositoryto discuss getting started.
Package: autodistill-yolo-world
Autodistill YOLO-World ModuleThis repository contains the code supporting the YOLO-World base model for use withAutodistill.YOLO-World, developed by Tencent's AI Lab, is a zero-shot object detection model that can run in close-to-real-time on powerful GPUs. You can provide arbitrary text prompts to detect objects in images, and the model will return bounding boxes and class labels for the objects it finds.You can use YOLO-World in Autodistill to detect objects.Read the fullAutodistill documentation.Read theYOLO-World Autodistill documentation.InstallationTo use YOLO-World with autodistill, you need to install the following dependency:pip3installautodistill-yolo-worldQuickstartfromautodistill_yolo_worldimportYOLOWorldModelfromautodistill.detectionimportCaptionOntologyfromautodistill.utilsimportplotimportcv2# define an ontology to map class names to our GroundingDINO 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=YOLOWorldModel(ontology=CaptionOntology({"book":"book"}))# predict on an imageresult=base_model.predict("bookshelf.jpeg",confidence=0.1)plot(image=cv2.imread("./bookshelf.jpeg"),classes=base_model.ontology.classes(),detections=result)LicenseThe YOLO-World model is released under aGPT-3.0 license.🏆 ContributingWe love your input! Please see the core Autodistillcontributing guideto get started. Thank you 🙏 to all our contributors!
Package: autodmri
Automated characterization of noise distributions in diffusion MRI dataThe example and documentationThe latest version can be installed withpip install autodmriYou can find a quick example and datasets overhereand the full documentation athttp://autodmri.rtfd.io/.An example, super basic call to the script would beautodmri_get_distributiondwi.nii.gzsigma.nii.gzN.nii.gzmask.nii.gzBe sure to check the options by passing--helpto the script.The manuscript and referencesYou can read thejournal versionin Medical Image Analysis and the datasets are available herehttps://zenodo.org/record/2483105.The conference version of the manuscript (as published in MICCAI 2018) is availableherefor free
and from thepublisher.Here is a bibtex entry for the journal version@article{St-jean2020_media,
author = {St-Jean, Samuel and {De Luca}, Alberto and Tax, Chantal M.W. and Viergever, Max A. and Leemans, Alexander},
doi = {10.1016/j.media.2020.101758},
eprint = {1906.12121},
issn = {13618415},
journal = {Medical Image Analysis},
month = {jun},
pages = {101758},
title = {{Automated characterization of noise distributions in diffusion MRI data}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841520301225},
year = {2020}
}and for the conference manuscript in MICCAI@InProceedings{St-jean2018_miccai,
author="St-Jean, Samuel
and De Luca, Alberto
and Viergever, Max A.
and Leemans, Alexander",
editor="Frangi, Alejandro F.
and Schnabel, Julia A.
and Davatzikos, Christos
and Alberola-L{\'o}pez, Carlos
and Fichtinger, Gabor",
title="Automatic, Fast and Robust Characterization of Noise Distributions for Diffusion MRI",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="304--312",
isbn="978-3-030-00928-1"
}Referencing the code itselfThe code is also autoarchived on zenodo for those wanting to refer to a specific version over here
Package: auto-dns
Auto DNS is a Python command line application which automatically updates the DNS records of your domain using various DNS providers.Supported DNS providers:ArvanCloudflare [coming soon]This library is intended to be used on a system with a dynamic public IP that you want to map to a static domain name.InstallationThe package can be installed using pip:$pipinstallauto-dnsOr if you prefer, you can use Poetry:$poetryaddauto-dnsConfigurationTo configure a DNS provider API key, use the following command:$autodnsset_api_key<provider><api_key>UsageTo create a DNS record:$autodnscreate<domain><record_type><name><provider>--ip<ip_address>To update a DNS record with your current public IP, use the following command:$autodnsupdate<domain><record_type><name><provider>--ip<ip_address>To delete a DNS record:$autodnsdelete<domain><record_type><name><provider>To get DNS records:$autodnsget_record<domain><provider>--record-type<record_type>--name<name>ContributingContributions are welcome! Please feel free to submit a Pull Request.LicenseAuto DNS is released under the MIT License.