Changelog

Picsellia SDK Python is a library that allows users to connect to Picsellia backend.

All notable changes to this project will be documented in this file.

[6.24.0] - 2025-07-28

Added

• DatasetVersion.list_assets() can be called with parameters assignment_status, assignment_user_id and assignment_step_id, to filter on assets in campaign.
• DatasetVersion.list_import_coco_file() will create LabelGroup if field supercategory of categories object are set, and that force_create_label is True

DataProjection

• DataProjection objects represent projections of Data on the platform. They can be downloaded or deleted.
• Datalake.create_projection() now returns a DataProjection
• Data.list_projections() can be called to retrieve DataProjection objects linked to a Data

LabelGroup

• LabelGroup objects represent groups of labels on the platform.
• LabelGroup.get_parent() can be called to retrieve the parent of the group
• LabelGroup.set_parent() can be called to set the parent of the group
• DatasetVersion.create_label_group() can be called to create a label group
• DatasetVersion.list_label_groups() can be called to list all label groups of a dataset
• Label.get_group() can be called to retrieve the group of a label
• Label.set_group() can be called to set the group of a label

Changed

Worker Deprecation

As we are moving to a new user management system, the Worker class will progressively be deprecated.
The Worker class existed before our Annotation Campaign system to create multiple annotations on assets.
Since it is no longer possible to create multiple annotations without the campaign system, we can remove many filters and parameters from SDK methods.

Methods that will no longer use the given worker parameter:

• Annotation.update()
• Asset.get_annotation()
• Asset.create_annotation()
• DatasetVersion.build_coco_file_locally()
• DatasetVersion.export_annotation_file()
• DatasetVersion.import_annotation_*_file()
• DatasetVersion.list_annotations()
• DatasetVersion.load_annotations()
• Experiment.compute_evaluation_metrics()

Methods that will raise an error if a worker is provided:

• Asset.delete_annotations()
• MultiAsset.delete_annotations()
• DatasetVersion.delete_all_annotations()

Important note: Worker.id will no longer be the real worker ID. It will be either a UUID representing 0 or a user ID. You should not use it anymore.

Migration to User class:
Do not use the Worker class anymore. Instead, use the User class, which manipulates real users of the platform.

• Dataset.list_workers(), Dataset.find_worker(), and Deployment.list_workers() will return Worker objects but should not be used anymore
• Instead, use Dataset.list_users() and Deployment.list_users() which have been added
• You can also use Client.list_users() to get users from your organization
• You can call Dataset.add_user() and Deployment.add_user() to add a User to a resource. This is useful for UNPRIVILEGED users that you want to add as LABELER on campaigns
• In Campaign.add_step() and Step.update(), assignees parameters now accept a User and a user ID

[6.23.0] - 2025-07-01

Added

• Datalake.upload_data() will accept parameter upload_dir when using a private object storage. You can now specify the directory path on the object storage
• Deployment.monitor(), Deployment.monitor_bytes(), Deployment.predict() and Deployment.predict_bytes() will also accept this upload_dir parameter
• Processing.create_dataset_version_processing_constraints() can be called to create constraints on dataset version processings
• Processing.create_datalake_processing_constraints() can be called to create constraints on datalake processings

Fixed

• Data.load_embeddings and Asset.load_embeddings were not working anymore due to a minor change in api
• When reading exif data of images, resolution_unit was not correctly read.

[6.22.0] - 2025-06-16

Added

• DatasetVersion.start_fast_training() to start a fast training on a DatasetVersion

• DatasetVersion.list_fast_trainings() to get FastTraining objects linked to this DatasetVersion

• FastTraining.list_evaluations() to get evaluations done during this training

• FastTraining.list_logs() to list logs done during this training

• FastTraining.get_log() to get a log done during this training

• FastTraining.get_parameters() to get a parameters used by this training

• FastTraining.list_artifacts() to get files of the model version used by this training

• Deployment.update() allows parameter sample_rate which can be used to not send all images to platform, but still computing monitoring data

• DatasetVersion.launch_processing() now allows to select model_version_id and target_version_name used during processing

• Datalake.launch_processing()now allows to select model_version_id and target_datalake_name used during processing

[6.21.8] - 2025-05-16

Changed

• On requests.ConnectionError, all api calls will retry up to 5 times with an exponential backoff. It means it will wait up to 7.5 seconds.

[6.21.7] - 2025-05-14

Added

• AssetLister and DataLister now have a list_ids() method. This is an internal method that should be used with care and can change in the future

[6.21.6] - 2025-05-14

Changed

• On requests.ConnectionError, all api calls will retry up to 4 times with an exponential backoff

[6.21.5] - 2025-04-17

Added

• DatasetVersion.compute_shapes_embeddings() will wait for job to end by default
• DatasetVersion.activate_visual_search() will wait for job to end by default

[6.21.4] - 2025-04-15

Experimental

• Features for shape embeddings on DatasetVersion were added

Added

• Datalake.embeddings_computation_status() to fetch status of embeddings computation on Datalake
• DatasetVersion.embeddings_computation_status() to fetch status of embeddings computation on DatasetVersion
• DatasetVersion.compute_shapes_embeddings() to start computation of shape embeddings on DatasetVersion
• DatasetVersion.delete_shape_embeddings() to delete computation of shape embeddings on DatasetVersion
• DatasetVersion.get_shapes_embeddings_status() to fetch status of shape embeddings computation on DatasetVersion
• DatasetVersion.list_rectangles_embeddings() to fetch rectangle embeddings on DatasetVersion
• DatasetVersion.list_polygons_embeddings() to fetch polygon embeddings on DatasetVersion

[6.21.3] - 2025-04-09

Added

• To prepare next release of picsellia-agents, we need some internal methods

[6.21.2] - 2025-03-31

Changed

• Upload of large files will be retried 3 times on Read Timeout
• Better handling of large annotation files on all import_annotations_file methods.

[6.21.1] - 2025-02-24

Fixed

• An annotation type hint was causing bug with python >= 3.11

Docs

• Some minor doc fixes

[6.21.0] - 2025-02-20

Some features around Visual Search !

Changed

• Remove detection_boxes in SegmentationPredictionFormat

Added

• Datalake.visual_search() can be called to search similar data
• Datalake.text_search() can be called to search data that match a text query
• Datalake.count_embeddings() can be called to count number of data indexed
• Datalake.list_embeddings() can be called to list embeddings of in your datalake
• DatasetVersion.activate_visual_search() can be called to activate visual search in your dataset
• DatasetVersion.deactivate_visual_search() can be called to deactivate visual search in your dataset
• DatasetVersion.count_embeddings() can be called to count number of data indexed in your dataset
• DatasetVersion.list_embeddings() can be called to list embeddings of in your dataset
• Data.embeddings property can be used to retrieve stored embeddings
• Asset.embeddings property can be used to retrieve stored embeddings

Fixed

• Deployment attribute _review_campaign_id was not well-defined in some cases

[6.20.0] - 2025-01-20

Happy new year you all !
Some new features around campaigns, some refactoring, some breaking changes in this version as deprecated code was removed.

Breaking

Behavior has changed for users that initialize Client() without giving organization_name or organization_id

• Users that belong to only one organization will be connected to this organization.

• Users that belong to multiple organizations but own one will be connected to owned organization.

• Users that belong to multiple organizations but do not own any organization won't be able to initialize an unspecified Client.

• AnnotationCampaign.list_steps() will return an AnnotationCampaignStep instead of a dict!

• Model.type and Model.framework were deprecated and are removed from now.

• Datalake.find_all_datas() was deprecated and is removed from now. You should use list_data() instead.

• DatasetVersion.find_all_assets() was deprecated and is removed from now. You should use list_assets() instead.

• ModelContext.retrieve_experiment() was deprecated and is removed from now. You should use get_experiment() instead.

• Deployment.check_feedback_loop_status() was deprecated and is removed from now.

• Deployment.disable_feedback_loop() was deprecated and is removed from now.

• Deployment.setup_continuous_training() will not use deprecated parameter dataset_version anymore.

• Experiment.publish() was deprecated and is removed from now. You should use export_as_model_version() instead

• ModelVersion.update_thumbnail was deprecated and is removed from now. You should use the web app instead.

Added

• AnnotationCampaignStep model, to manage your campaign step
• Client.get_processing() to find a processing by its name
• AnnotationCampaign.sync_assets() can be called to add assets that are not assigned as task in your campaign
• AnnotationCampaign.update() can be called with default_entry_step and annotated_entry_step parameter
• AnnotationCampaignStep.redistribute_assignments() can be called to add assets that are not assigned as task in your campaign
• AnnotationCampaignStep.update() can be called to update a step
• AnnotationCampaignStep.delete() can be called to add assets that are not assigned as task in your campaign
• Client.list_annotation_campaigns() can be called to list annotation campaigns of your organization
• Client.list_review_campaigns() can be called to list review campaigns of your organization
• Worker class has now a type : DATASET or DEPLOYMENT
• Worker.user_id property
• Deployment.list_workers() can be used to list workers of a deployment
• ReviewCampaign and ReviewCampaignStep models can be used to handle campaigns on Deployment ! Methods are more or less the same, except for some edge case behaviors.
• Client.create_processing() can be called with description parameter
• DatasetVersion.copy_assets_to() can be called to copy assets from a dataset to another

Changed

• DatasetVersion.list_assets() should behave better when a list of parameter is given
• Datalake.list_data() should behave better when a list of parameter is given
• DatasetVersion.add_data() will add data by batch, so it won't fail with 413 error when more than 300k assets are given
• DatasetVersion.add_data() will now wait for job to end by default
• DatasetVersion.fork() will now wait for job to end by default
• AnnotationCampaign.create_step() can be called with sample_rate parameter

Removed

• AnnotationCampaign do not require name anymore

Fixed

• ModelVersion str method was not displaying well its origin name
• Handle 413 errors (when payload or uri are too large)
• Some bad generated documentation should be fine now
• Some methods were not reading filenames correctly on Windows

[6.19.2] - 2024-11-06

Added

• DatasetVersion.find_asset() can be called with id parameter to find an asset by its id.

[6.19.1] - 2024-10-31

Happy Halloween !

Added

• Deployment.find_predicted_asset() to find a PredictedAsset from different criteria

[6.19.0] - 2024-10-30

We dropped support of python3.8, goodbye old friend, you will be missed.

Added

• Deployment.predict_shadow() to predict with shadow model on an already processed PredictedAsset
• Deployment.monitor_shadow() and Deployment.monitor_shadow_from_oracle_prediction_id() to monitor with shadow model on an already processed PredictedAsset
• Added support of python3.13
• Bumped some packages
• While dropping python3.8, we could get rid of old syntaxes
• Better handling of conflict when monitoring already processed data
• A GitHub workflow so we can send to our Slack channel a release note

Changed

• We welcomed ruff as a new pre commit linter
• As we do not allow multi campaign on the same dataset, we changed DatasetVersion.get_campaign()

Fixed

• We don't warn anymore if you have a future version of the SDK

Deprecated

• We dropped support of python3.8

[6.18.3] - 2024-10-01

Added

• New processing types allowed on the platform : data auto tagging, model compression, model conversion

Fixed

• Deployment.predict() and Deployment.predict_bytes() were failing when sending metadata

[6.18.2] - 2024-09-17

Fixed

• We refactored upload of large file to allow upload very large file on Google cloud storage

[6.18.1] - 2024-09-05

Fixed

• Added Asset.content_type, needed in Asset.to_data_schema()

[6.18.0] - 2024-09-04

Added

• Deployment.predict_cloud_image() can be called to predict onto an image stored on your object storage
• Deployment.predict_data() can be called to predict onto a data stored on your target datalake
• Deployment.monitor_cloud_image() can be called to monitor an image stored on your target datalake
• Deployment.monitor_data() can be called to monitor a data stored on your target datalake
• All Deployment.predict() can be called with metadata dict parameter. It will be stored on Data after being processed.
• Deployment.monitor() can be called with metadata dict parameter aswell.
• All Deployment.predict() can be called with monitor boolean parameter. It allows you to only call inference service, it won't call our monitoring service.

[6.17.1] - 2024-08-22

Fixed

• On DatasetVersion.build_coco_file_locally() area of segmentation will now be computed properly

[6.17.0] - 2024-07-29

Added

• Experiment.launch() now return a Job
• PredictedAsset class can be used to manage predictions of a Deployment.
• PredictedAsset.add_review class can be used to add review to assets of a Deployment.
• Deployment.list_predicted_assets() can be used to list assets of a Deployment.
• Processing class can be used to manage your processings
• Client.list_processings() can be used to list your processings.
• DatasetVersion.launch_processing() can be used to launch a processing on your DatasetVersion.

Fixed

• On Datalake.upload_data() with parameter fill_metadata=True could not pass validation of our api in some case, so we need to cast values.
• DatasetVersion.list_assets() with parameter filename_startswith was not working
• DatasetVersion.build_coco_file_locally() will only use the LAST annotation created on each asset. In campaign mode it caused some problems

Changed

• Some apis were deprecated on the backend side so we changed it on the SDK. These apis will be removed in the future : SDK versions prior to 6.17.0 might break.
• Allowed version of Pillow is now ">=9.2.0, <11"

[6.16.0] - 2024-06-05

Added

• DatasetVersion.list_assets() can now be called with a list of data ids
• DatasetVersion.import_annotations_coco_file() can now be called with use_id. If you exported your coco file with use_id, you can use import it now.
• DatasetVersion.import_annotations_coco_video_file() can be called to import video annotations in your dataset. You could export it from picsellia with the SDK since the 6.13.0
• Rectangle, Classification, Polygon, Line, Point have now text field that you can use as an OCR feature. You can call update on shape to update text field.
• DatasetVersion.import_annotations_coco_file and its video counterpart will read utf8_string field of json coco file and import it into the platform.

Fixed

• On Datalake.upload_data() with parameter fill_metadata=True could not pass validation of our api in some case, so we need to cast values.
• DatasetVersion.list_assets() with parameter filename_startswith was not working
• DatasetVersion.build_coco_file_locally() will only use the LAST annotation created on each asset. In campaign mode it caused some problems

Changed

• We upgraded pydantic dependency : we now depends on its version 2
• Default max pagination is reduced to 1000. It was 10000, it's a little bit too much
• We reduced to 1000 the max chunk_size on load_annotations()
• We refactored tests and their files

Deprecated

• We dropped support of python3.7
• All stuff related to Scan have been removed

[6.15.0] - 2024-05-06

Added

• Datalake.import_bucket_objects() can now be called with tags and source, that will be given to created data.

Fixed

• Typo on get campaign log
• wait_for_upload() had 2 beartype decorators
• Some tests were failing

[6.14.2] - 2024-04-19

Changed

• We now use session object to download and upload from presigned url. This should speed up a little bit, and not create enormous amount of connection.

[6.14.1] - 2024-04-16

Added

• Datalake.list_data() can be called with ids to fetch data from their ids.

Fixed

• When listing data and assets, filtering with tags and filenames was not working
• Downloadable objects such as Data, Asset and Artifact, were syncing before downloading, we don't want to do that anymore
• Type hint problem on AnnotationCampaign
• Documentation on AnnotationCampaign
• Tests on AnnotationCampaign

[6.14.0] - 2024-03-19

Added

• DatasetVersion.create_campaign() can be used to create an AnnotationCampaign.
• AnnotationCampaign.add_step() can be used to add a step to an AnnotationCampaign.
• AnnotationCampaign.launch() can be used to add assignments to all assets on this campaign of your DatasetVersion.
• AnnotationCampaign.update() can be used to update an AnnotationCampaign.
• DatasetVersion.get_campaign() can be used to retrieve the AnnotationCampaign of this DatasetVersion.
• DatasetVersion.export_annotation_file() can be called with parameter use_id=True. Generated files will use asset id insted of filename.
• Datalake.import_bucket_objects() can be used to import bucket objects of your S3 that are not yet on the platform.

Fixed

• Calling MultiAsset.as_multidata() with more than 10k assets was bugged
• Some dependencies were fixed on pyproject.toml, such as pyyaml, beartype and orjson. Python 3.11 and Python 3.12 can now be used with the sdk

[6.13.0] - 2024-02-26

Added

• Datalake.upload_data() can now handle unreadable images (like hdf5) and video ! By default, it will wait for data to be full processed by our services
• Datalake.create_projection(data) can be used to create a projection on a data : you have to give a name for this projection, it will be of type CUSTOM.
• DatasetVersion.export_annotation_file() can be called with boolean parameter export_video to only export video in annotation file

Fixed

• Default content type assigned to data we cannot read mime type is now application/octet-stream

[6.12.0] - 2024-01-17

Added

• Data, MultiData, Asset, MultiAsset can be downloaded with download(use_id=True) to use id as filename. Extension of base filename will be used.
• DatasetVersion.build_coco_file_locally() can now be called with parameter use_id=True to build a coco file with file_name keys as <id>.<extension>
• When initializing a Client, you can pass parameter session with your own requests.Session object, that will be used on each request done by the sdk. It allows configuring a proxy or custom headers.

[6.11.0] - 2024-01-10

Happy new year everyone !
Some minor features and fixes in this version

Added

• Data.update_metadata() can be called to update metadata of a data.
• DatasetVersion.load_annotations() can now be called with parameter assets to build a json coco file with only given assets.

Fixed

• When downloading a file, there was a race condition when creating parent directories of downloaded file.
• Storing an artifact with do_zip=True was not creating expected archive name when a dot was present in filename.
• On upload, large file discriminant is now 25MiB and not 5MiB

Changed

• Major changes in docstrings, a lot of fixes!

[6.10.3] - 2023-09-22

Fixed

• PredictionFormat will not raise an exception if one of its list is empty. Deployment.monitor() can now be called with empty predictions.

[6.10.2] - 2023-09-06

Fixed

• Deployment.predict_bytes() needs parameter filename to work. It was sending prediction with filename media.

[6.10.1] - 2023-08-31

Added

• DatasetVersion.build_coco_file_locally() now adds area of bounding boxes on generated files for Object Detection.

[6.10.0] - 2023-08-21

Some deprecation on privacy object creation, it is not possible to create a public dataset, project or model from the sdk anymore.
Attach dataset version to Feedback Loop and Continuous Training settings of your deployments.

Added

• Deployment.attach_dataset_to_feedback_loop() can now be called to attach a dataset to Feedback Loop settings of your deployment. Attached datasets can be filled with reviewed prediction from your deployment.
• Deployment.detach_dataset_from_feedback_loop() allows detaching dataset from Feedback Loop.
• Deployment.list_feedback_loop_datasets() will list attached datasets of your Feedback Loop.
• Deployment.attach_dataset_to_continuous_training() can now be called to attach a dataset to Continuous Training settings of your deployment. Attached datasets will be added to your experiment created when Continuous Training is triggered.
• Deployment.detach_dataset_from_continuous_training() allows detaching dataset from Continuous Training.

Deprecated

• Parameter dataset_version of Deployment.setup_feedback_loop()
• Parameter dataset_version and model_version of Deployment.setup_continuous_training()
• Parameter private of Client.create_model()
• Parameter private of Model.update()
• Parameter private of Client.create_project()
• Parameter private of Project.update()
• Parameter private of Client.create_dataset()
• Parameter private of Dataset.update()

[6.9.0] - 2023-07-10

Added

• Datalake.upload() can now be called with parameter fill_metadata. On upload, set this parameter to True to read exif metadata flags and push metadata to Picsellia. By default, fill_metadata is False.
• Client.get_datalake() can now be called with parameter id or name to retrieve the Datalake you want. If nothing is given, this method will retrieve your default datalake.
• Deployment.predict_bytes() can now be called if you want to send image as bytes on Serving service
• ModelVersion.update() and Model.create_version() can now be called with docker_tag parameter

[6.8.0] - 2023-06-12

Added

• Datalake.upload_data() now accept parameter metadata which is a dict (or a list of dict if there are multiple filepaths) matching common metadata allowed by Picsellia.
• DatasetVersion.fork() can now be called with parameters with_annotations and with_labels as web application
• DatasetVersion.export_annotation_file() can now be called with AnnotationFileType.YOLO to retrieve a zip with yolo files
• Dataset.create_model() and DatasetVersion.update() have parameter description to update description of a ModelVersion

Fixed

• When adding evaluation, allow empty list to be sent as a list of shape

[6.7.1] - 2023-05-15

Some changes on monitor() following an update of our monitoring stack.

Added

• Property type of a deployment.
• Method Deployment.monitor_bytes() can be called if you have an image as bytes to send to monitoring.

Changed

• Deployment.monitor() do not use parameters model_version and shadow_model_version anymore
• Parameter shadow_raw_predictions of Deployment.monitor() will be replaced by parameter shadow_prediction in the future
• You can add content_type parameter to monitor() if you don't want sdk to infer it with mimetypes. It will be checked with common content types of the mimetypes library
• content_type of monitor_bytes() is mandatory and should be a SupportedContentType enum or string. Current common type supported are "image/png" and "image/jpeg"

[6.7.0] - 2023-05-05

Added

• Add Client.create_deployment() to create a deployment. Allow user to create it without using Picsellia Serving
• Add DatasetVersion.retrieve_stats() to retrieve stats of your dataset version
• Add DatasetVersion.train_test_val_split() to split a dataset version into 3 different multi assets
• Add DatasetVersion.split_into_multi_assets() to split a dataset version into N multi assets and return their label repartition
• Add MultiData.split() and MultiAsset.split() to split from a given ratio a multi asset
• Add User-Agent with picsellia version in headers of requests

Changed

• Framework and type of Model are now configurable into ModelVersion
• Get by id now use parent API to ensures object are in the same organization as the one connected
• Methods manipulating tags on client are now calling other routes
• Some minor fixes on documentation

Fixed

• Segmentation format used in monitor() was not supported by monitoring service

[6.6.0] - 2023-04-06

Added

• list_data and list_assets have a new parameter q that can be used the same way the query language is used in the web platform
• Deployment has new methods: set_training_data, check_training_data_metrics_status and disable_training_data_reference, that can be used for monitoring and unsupervised metrics.
• as_multidata of MultiAsset can now be called with parameter
• Artifact, Data, Asset, LoggingFile, ScanFile, ModelFile are now inheriting from Downloadable, and have url property that can be used to download files. These urls are presigned and expired at some point in the future.
• Methods add_evaluation, list_evaluations and compute_evaluations_metrics of Experiment can be used to add, list and compute evaluation of an Experiment

Changed

• Deployment Feedback Loop dataset is now only used as a recipient for new assets submitted after review in the predictions dashboard
• bbox of COCO annotation cannot be a tuple anymore

[6.5.0] - 2023-03-15

Added

• Jobs are now handled differently in Picsellia platform
• get_dataset_version_by_id can be done in Client
• get_model_version_by_id can be done in Client
• get_experiment_by_id can be done in Client
• Import yolo files with import_annotations_yolo_files
• upload_data can be called with an ErrorManager to retrieve errors after upload.

Fixed

• Pascal VOC files parsing allows some fields to not be set
• 502 errors were not handled correctly
• Uploading images that were transposed now correctly send width and height on the platform

Changed

• Downloading files has a new retry policy
• When importing YOLO, COCO or Pascal VOC files, you need to set type of dataset before.
• Some refactor on import annotations, it should be faster now !

[6.4.2] - 2023-03-03

Changed

• Return line after logging chapter
• Allow parameter enforced_ordered_categories on build_coco_file_locally() to enforce categories of built coco file.

Fixed

• Do a paginated call in dataset version train_test_split when loading assets
• Method set_type of dataset version was failing when logging result if a string was given

[6.4.1] - 2023-02-17

Fixed

• Some methods were using types not compatible with python older than 3.8

[6.4.0] - 2023-02-16

Added

• DatasetVersion list_assets() will call XGET method when filtering on filenames or object_names, to not have the limit size query error.

Fixed

• Datalake upload_data() now allow effectively a Datasource
• Import annotations was limited to 10 000 annotations because find_assets() was used. It is now using list_assets() which is paginated.

Changed

• Deployment update() cannot change active anymore.

Deprecated

• DatasetVersion find_all_assets()

Experimental

• DatasetVersion build_coco_file_locally() will create a COCOFile object that can be written in a json file

[6.3.2] - 2023-01-31

Added

• Experiment.export_in_existing_model() method will create a version of given model when exporting experiment.
• Datalake list_data() can be used to filter on object_names.

Fixed

• ModelVersion.store() was not using replace parameter correctly, it will now delete existing model file if it exists

Changed

• All max_workers parameters are now None by default, to use cpu count + 4 as default value
• Datalake list_data() will call XGET method when filtering on filenames or object_names, to not have the limit size query error.

Removed

• AnnotationStatus.REVIEWED status was never really used and is now removed from Picsellia.

[6.3.1] - 2023-01-20

This is a patch to fix download dataset on huge datasets

Fixed

• as_multidata now uses xget to retrieve all datas with given list of ids

Changed

• as_multidata is not called in download from DatasetVersion

[6.3.0] - 2023-01-19

Happy new year from the Picsellia Team !
This minor version add some useful methods and fix specified COCOFile format.

Added

• List public models with list_public_models() from the Picsellia Hub in Client.
• Find public model with find_public_model() from the Picsellia Hub in Client.
• Convert MultiAsset into MultiData with as_multidata() and as_list_of_data()
• Allow asset tags to be added when adding data to a dataset with add_data()
• Method __repr__ has been added to MultiObjects
• Property labels in ModelVersion objects
• Add get_dataset in ModelContext to get DatasetVersion from its attached name

Fixed

• breaking: COCOFile was wrongly specified: in annotations, only one segmentation was allowed for each shape,
  but COCOFile allows multiple polygons. Now you need to give a list of list of coordinates instead of only one list of coordinates.
  import_annotations_coco_file() should now work fine with COCO files with segmentation.
• Add AuthenticationError again in exceptions, which was used by picsellia-tf2

Changed

• may be breaking: Default value of force_create_label is now True in import_annotations_coco_file and import_annotation_voc_file.
• Rename retrieve_experiment into get_experiment in ModelContext
• retrieve_datasets in ModelContext now return a dict with attached name as key and dataset version as value
• download() a DatasetVersion is not calling assets extended endpoints which was taking a lot of time on huge datasets.
• Imports order with pre-commit cln tool
• Doc generation with all properties docstring

[6.2.1] - 2022-27-12

Fixed

• Fixed .predict() behavior by removing mandatory parameters tags and source

[6.2.0] - 2022-27-12

Added

• Add docker env variables support on ModelVersion
• Add an experiment.log_parameters() to use experiment.log() with parameters more easily
• Add possibility to attach base parameters when experiment.attach_model()
• Add feedbackloop method check status
• Add __repr__ methods to DAO objects, for better UX representation
• Add possibility to set tags and source when sending a prediction to serving
• Add get_or_create_datasource() in client

Changed

• Rename experiment.publish() into experiment.export_as_model()
• Some changes on feedbackloop methods
• Method list_data and list_assets can now use parameter intersect_tags to find objects that have all tags given
• Allow Path object in every method that accept path as string
• Some exceptions were renamed, some useless were removed

[6.1.2] - 2022-11-10

Fixed

• Use of typing.List instead of list to support python < 3.9

[6.1.1] - 2022-11-10

Added

• Add convert_tags_to_classification() in dataset version
• Add get_data_tags() on asset

Changed

• Github workflow

[6.1.0] - 2022-11-01

Added

• Add ONXX in Framework enums
• Possibility to use strings instead of enums in methods

Changed

• Make duration optional in annotation creation/update/overwrite
• Make duration int compatible
• log() method now create, update or append a log, in only one call to backend
• Remove create_log method
• Optional type hint is now used everywhere
• Prevent user from updating log image via log.update(), only experiment.log can be used for this case

Fixed

• In log() method, image type data stored bad values

[6.0.2] - 2022-10-17

Added

• Logging File for an Experiment

Changed

• A lot of typos, variable naming, minor formatting
• String .format() into f-strings
• Old package dependencies
• train_test_split() return MultiAsset instead of List[Asset]

Fixed

• Regeneration of JWT when expired
• When downloading file, open file only if response is ok

[6.0.1] - 2022-10-04

Added

• CHANGELOG file.

Changed

• Fixed test_train_split with breaking changes of query language in list assets.
• Documentation of core functions, minor typos fixes.
• Workflow for testing on staging when push on development and main.

[6.0.0] - 2022-10-03

Added

• Annotation are now objects storing Rectangle, Polygon, Classification, Point and Line for an Asset
• Artifact are now Experiment files stored
• DatasetVersion are now used as versions of Dataset
• Datalake objects
• Datasource objects
• Job objects, to wait for tasks to end
• ModelVersion are now used as versions of Model
• ScanFiles are now Scan files stored

Changed

• Pictures renamed to Asset that are Data objects in a DatasetVersion
• Multithreading usage
• Tags
• Annotations with objects