Support for PASCAL VOC? #26
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Currently you can convert VOC annotations to the COCO format. We will support VOC-style annotations as well as custom datasets. |
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Thanks for the response. 👍 |
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druzhkov-paul
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Jun 17, 2020
fixed requirenments since code cannot be compiled with pt1.5
liuhuiCNN
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May 21, 2021
* Add cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms model on oidv5 and obj365. * Add obj365 config annotation * Update docs.
FANGAreNotGnu
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Oct 23, 2023
* remote resource management * add files * remote resource management * distributed scheduler
FANGAreNotGnu
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Oct 23, 2023
* init * Adding Hyperband (open-mmlab#4) * refactor changes * grammer * asyc hyperband * Initial commit (open-mmlab#5) * Add dataset sanity check (open-mmlab#7) * release resources (open-mmlab#6) * Add dataset histogram viz and check (open-mmlab#8) * Add dataset histogram viz and check * Add matplotlib in setup * Checkerpoint (open-mmlab#10) * release resources * rename example fils * keep track of the best result * serialization * save load * add util * checkpoint and resume * keeping task id * terminator state * rm comments * Add autogluon backend fit and refine apis (open-mmlab#9) * add autogluon backend fit and refine apis * update * update * add some doc * refine * refine fit * refine fit * refine fit * add guideline (open-mmlab#11) * Add Plots for Visualization (open-mmlab#12) * add plots * current progress * rm comment * Refine fit (open-mmlab#13) * refine fit * minor update * fix setup (open-mmlab#14) * fix guide (open-mmlab#15) * Add autogluon notebook (open-mmlab#16) * add notebook * update notebook * Demo patch 1 (open-mmlab#17) * mv dataset inside * patch * Demo (open-mmlab#20) * fix * fix 1 * add notebook * setup (open-mmlab#19) * Fix Checkerpoint (open-mmlab#22) * resource * mv tasks into object method * Fixtypo (open-mmlab#23) * Revert "Demo (open-mmlab#20)" This reverts commit a8fa993b461b8cd424edbe772fe6b0264f6ee79a. * fix * Update AutoGluon Notebook (open-mmlab#24) * Update notebook * remove * raise warning for resource (open-mmlab#25) * [WIP] AutoGluon Distributed (open-mmlab#26) * remote resource management * add files * remote resource management * distributed scheduler * add autogluon.distributed scheduler (open-mmlab#28) * add cifar script and tensorboard (open-mmlab#27) * patch for state-dict (open-mmlab#29) * distributed with ssh helper (open-mmlab#31) * ssh helper for distributed * tutorial * Refactor api and update image classification results (open-mmlab#30) * refactor mxboard api and update img classification results * Update notebook to work on mac * update notebook and compact svg * Multiprocess Queue Support MacOS (open-mmlab#33) * Queue for Mac OS * add queue * Backend Tutorials (open-mmlab#32) * init tutorial * add figures * add figures * add comments * merge and demo * add plot * img path * Refine notebook and add dataset statistics (open-mmlab#34) * refactor mxboard api and update img classification results * Update notebook to work on mac * update notebook and compact svg * refine notebook and dataset * add conda * rm ipynb * update notebook and dataset * uncommnent dist * notebook results update (open-mmlab#35) * Add MINC experiments and Refine Data Loss Metric (open-mmlab#36) * add minc exp * fix bug * add auto loss and metric * update minc results * fix kwargs (open-mmlab#37) * Refine auto Dataset, Nets, Losses, Metrics, Optimizers and Pipeline (open-mmlab#38) * add comments * fix * refine dataset * Add Kaggle Shopee Classification script (open-mmlab#40) * add kaggle shopee img classification example * update results * Update .gitignore * Distributed FIFO and Bug Fix (open-mmlab#39) * simple visualizer * distributed scheduler progress * local node message * distributed fifo okay * Add local helper (open-mmlab#42) * add local helper * Add Distributed ImageNet Example (open-mmlab#43) * fix img dataset (open-mmlab#45) * Add object detection (open-mmlab#41) * add object detection voc * fix * update results and fix some issues * fix search space * update obj detection results * Dist-hyperband, Doc and Tutorial (open-mmlab#48) * dist hyperband * add docs * Refactor fit and dataset api (open-mmlab#50) * advance api * initial commit * status * advance api initial commit rm * fix example issue (open-mmlab#51) * current progress * save model params (open-mmlab#53) * add save model params * add missing file * resume at any point * add missing import * fix hyperband * dist not implemented * add tutorial doc (open-mmlab#55) * mxutils * add example and notebook * add fit tutorial * add notebook file * Text Classification (open-mmlab#6) * Initial commit for Text Classification classes * Added results obejct in core.py * Added Estimator package * Rebase * Added PyTest_Cache to git ignore * Added FTML Optimizer * Added impl for core.py * Added method signatures for text classification model zoo * Added typing hints to nets.py * Wrapped up implementation of dataset to yield dataloaders * Added TextData Transforms and Dataset Utils * Added impl for pipeline * Fixed errors + formatting commit * Added beginner example for text_classification for sst dataset * Added handler for data loader * Refined DataLoaderHandler * Printind the exception stack trace if any * Replaced print with logs * Fixed syntax error * Changed default GPU counts * Changed trial scheduler to default * Changed Max_Training_Epochs to 5 * Fixed syntax error for string formatting * Added metrics to the reporter * Fixed reporter issues * Uncommented plot training curves * Fix import error * Made reporter a handler * Fixed args issue * Added exponential search space * Added batch_size as a hyperparam to dataset class * Added more models to text_classification * Removed big rnn for now * Added rules for tokenization of text data * Now printing network architecture as well * Changed the rules for tokenization * Added Dropouts and Dense Layers as a hyperparam * Added todo to fine tune LM * Changed upper bound for batch size * Now printing task ID as well along with the result * Now added task ID to the reporter as well * Added num_gpus to the args * Added unit tests (dummy for now) * Added skeleton for autogluon initializers * Added demo jupyter notebooks * Updated IMDB notebook * Updated Demo notebook for Stanford Sentiment Treebank dataset * Added NER base structure * adding pipeline + model zoo for NER * adding LR warmup handler * NER CoNLL2003 dataset * NER dataset format conversion * Added NER HPO codebase * adding core + example for NER * update pipeline, dataset, core * fixes * add eval helper code * move data proc code to utils, fixes * Added WNUT2017 dataset support * fix num_classes * fix num_classes * add bertadam optimizer * pre-defined parameters * Increased the maximum sequence length * move helper code to task utils * Modified dataset preprocessing code * fix class name, rebase * fix * Added comments for modifying and copying the NER data methods from GluonNLP toolkit * The WNUT-2017 dataset now downloads automatically, user just needs to pass the dataset name * pylint check(round 1) * pylint check(round 2) and import seqeval library for fetching some NER data methods * add multi-gpu support * pylint check(round 3) * Minor coding formats fix * fix multi-gpu, working version * Cleanup * Minor code fix * add default params for datasets * Minor contructor fix * update default seq len for wnut17 * adding demo notebook * update demo notebook * update notebook * add early stopping * update net construction config * Initial commit for making MXBoard/TensorBoard as a handler to pass to the estimator * Added TensorBoard requirements * Added TensorBoard support to Text classification in the form of a handler * Refactored the transforms, speeding up the data len functions * Added dataset name for BERT * Added BERTAdam optimizer for BERT * Added BERT Networks * Added BertClassifier block * Added support for Bert Model to the pipeline * Added DataLoader handler for BERT * Now passing BertDataLoaderHandler to the Estimator, instead of using SentimentDataLoaderHandler() * Added support for BERT Models and refactored pipeline.py in text-classification * Bunch of pycharm formatting changes * Added example classes for Glue SST2, MNLI and Yelp Datasets * Fixed a typo for val set * Fixed LR range issue * Fixed missing argument to function call * Fixed typo in model_zoo * add support for ontonotes-v5, auto max seq len, cleanup * [WIP] Unittest for Named Entity Recognition * Added more unittest for Named Entity Recognition * adding NER integration tests * Added more integration test methods for NER * Added nosetest module for NER * fix nets, optims, batch_size api for NER & add advanced user example * refactor Scheduler to pull out Terminator * add missing files/fix Terminator * set cpu affinity * assign cpu affinity within the task * integer casting * terminator updates * adding jnlpba, bc5cdr datasets + fixes * Moved dataset/utils to text_classification_dataset/utils * Added placeholder for buildspec.yml and pylintrc * Refactored setup.py * Added bdist info * Fixed setup.py issue * Added requirements.txt and reading it in setup.py * Fixed wrong mapping of Sent : Label when reading tsv dataset * Added Train Field indices and Val Field indices for TSV Datasets as kwargs * Fixed issue of loading data lengths by using multi processing * Added LR Warmup Handler use to Text Classification's pipeline * Now plotting Train metrics as well at epoch end as well as fixed index issues while reading MNLI dataset * Added support for GLUE - MRPC Dataset * Updated the download dataset method * Removed vocab getters and setters from dataset * Now loading json files as SimpleDataset and removed methods to load dataset from gluonnlp * Moved transforms from dataset to task * Added num_workers parameter to the dataset * Added losses/metrics and moved dataset class inside TC.task * Removed core.py as it's not needed anymore * Reduced code duplication by creating a lightweight dataset class * Added MXBoard Handler to estimator * Removed uncommented code for fifo scheduler * Now printing the exception along with its stacktrace * Now printing the exception along with its stacktrace cr https://code.amazon.com/reviews/CR-11188375 * Added reading of datasets in .txt format * Removed NER task from the CR * Added support for multi-sentence in TextDataTransform * Added support for multi-text datasets * Added task specific optimizers for text classification * Removed task-id from the reporter * Removed task_id and EPOCH_END callback from reporter * Removed big RNN and en_de_transformer * Now making DataLoaderHandler a single class * Removed ClassificationHead class * Renamed init_env to init_hparams * Removed MXBoard Handler * Separated model, dataset, transforms from the method * Added dataset.py to read GluonNLP Datasets * Refactored the dataset class * Fixing import issues * Undoing formatting changes * Undoing formatting changes * Fixed issue with return of Batchify_Fn * Now updating validation dataset labels as well * Removed extra files from examples folder * Undoing the CI changes * Removed split and load and instead now calling nlp.split and load * Removed initializer folder for now * Removed Exponential * Added _Dataset to read the different formats * Undoing formatting changes * Removed unused files * Added deleted import for version * Now passing results back to scheduler via reporter * Addressed PR comments * Refactor get_transform_fn into task.dataset * Addressed PR Comments * allow uploading files * exception handl * reporter * add train val split * split reko datasets * handle pipeerror * wip * rm unused * advanced API * rm print * try error * import ok * wip cifar training ok * advanced api wip * add missing file * call method * current progress * controller sample okay * rl progress, controller sample okay * rl cifar example training okay * rm comment * Skopt searcher (open-mmlab#4) * Added skopt_searcher.py for BayesOpt search routine + unit-test comparing this searcher against the RandomSampling searcher on a toy optimization problem. Remaining TODOs: 1) include script to benchmark skopt_searcher against Hyperband in real autogluon image-classification task. 2) There is an issue that get_config() may become stuck in infinite while loop (for all searchers). There is no termination condition to handle the case where all possible configs have already been tried (should be inherited from BaseSearcher or Scheduler should automatically terminate). * edited BaseTask to allow for skopt Bayesian Optimization hyperparameters search via additional searcher argument value 'bayesopt'. Added train_cifar10.py example to compare random search with BayesOpt search (under default settings for all other flags in this script). Results are in new table added to scripts/image_classification/README.md * Rebased master into skopt. Cleaned up documentation/comments to be more presentable * rl training * viz * rl controller running okay * rl controller state dict ready * add dependencies * update fit etc * test pipeline * merge hackthon docs * add nas progress * reorganize folders * working progress * major features * Hackathon version freeze (open-mmlab#11) * add image classification notebook and update api doc * address comments * update * update result * add functinoalities * update tutorials * update tutorial * update * update * update mds * update fit etc * update fit hackathon * update * handle pipeerror * update+ * add skopt * add searcher and scheduler notebooks * New version of image_classification_searcher.md Has a couple of remaining TODOs. The biggest issue I see is that fit() in base_task.py cannot take any keyword arguments for constructing the Searcher. * how to pass keyword args to searcher removed all TODOs as well. This notebook execution still needs to be tested with the new base_task.fit code that uses 'searcher_options' as a kwarg. Something that is still missing from this tutorial is what is the hyperparameter search space that is actually being searched here? A curious user probably wants to know this information. I would add a short section right before "## Random hyperparameter search" to clarify this, for example: By default, `image_classification.fit()` will search for hyperparameter values within the following search space: # TODO: explicitly list the default search space. * added searcher_options * add lr scheduler * update example * Update image_classification_scheduler.md * minor typo correction dict definitions for searcher_options corrected * fix own dataset * update large dataset test * fixed skopt bug to handle ValueError exception * Update image_classification_scheduler.md * Update image_classification_scheduler.md * try to fix large data * address comments * test large data * fix pipeline * test predict batch * fix * test pipeline * img name * Freeze autogluon version * fit running * add utils * fit running, pending final fit * evaluate * docs * fix merge error * fix merge error * fix * fix * init method for autogluon object attr * docs compiled * documentation * rm sub docs * address merging error * address soem comments * choice * choice * fix typo * docs improvement * address comments * change list with choice * rename * fix typo * address some comments
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Are there any plans to support PASCAL VOC data set?
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