Datumbox

Datumbox offers an innovative Machine Learning platform specialized in Natural Language Processing.

[Vasilis Vryniotis @bbriniotis]

I've open-sourced VernamVeil, an experimental cipher written in pure Python, designed for developers curious about cryptography’s inner workings. It’s only about 200 lines of Python code with no external dependencies other than standard Python libraries: https://t.co/XqwIsDhWqU

[PyTorch @PyTorch]

TorchVision’s newest API addresses a long-standing request from the community. Read on to find out how it can be used to list and initialize available models & weights by name: https://t.co/WSTxns97EL

[SovitRath5 @SovitRath5]

https://t.co/RtQDjmOmbk
New post on DebuggerCafe - Comparing PyTorch ImageNetV1 and ImageNetV2 Weights with Torchvision 0.13
#PyTorch #Torchvision #DeepLearning #TransferLearning

[Vasilis Vryniotis @bbriniotis]

We just merged to TorchVision the SwinTransformerV2 & MViTv2 models, along with pre-trained weights:
- swin_v2_t 82.1%
- swin_v2_s 83.7%
- swin_v2_b 84.1%
- mvit_v2_s 80.8%
Many thanks to Ren Pang and Yanghao Li for their work:
https://t.co/h11IqbooA0
https://t.co/dkpVl1WfCN

[SovitRath5 @SovitRath5]

https://t.co/ywsqDfygey
New post on DebuggerCafe - Model Library Updates with PyTorch 1.12. A summary of the major changes that took place with new PyTorch 1.12
#PyTorch #torchvision #DeepLearning

[Paul King @paulk_asert]

Natural Language Processing with Groovy, OpenNLP, CoreNLP, Nlp4j, Datumbox, Smile, Spark NLP, DJL and TensorFlow
https://t.co/mMEmQoOEbh
#groovylang @ApacheSpark @ApacheGroovy @deepjavalibrary @TensorFlow @ApacheOpennlp @stanfordnlp @haifengl @Datumbox @oyahiroki

[Vasilis Vryniotis @bbriniotis]

TorchVision v0.13 and @PyTorch v1.12 released! 🥳
- New Multi-weight support API with meta-data & preprocessing transforms
- Swin Transformer & EfficientNetV2 models
- New improved pretrained weights for all popular models
- New AugMix, DropBlock & losses
https://t.co/iSIfDELT2d

[Vasilis Vryniotis @bbriniotis]

We have just merged the MViT architecture in TorchVision. This transformer video model achieves an 78.5% accuracy on Kinetics400. https://t.co/CDqPj1K5rU
Image variants to be released soon. Many thanks to the @PyTorch Video team for their support!
#ComputerVision #MachineLearning

[Vasilis Vryniotis @bbriniotis]

We just merged to @PyTorch's TorchVision an experimental implementation of the Simple Copy-Paste data augmentation that aims to improve the detection models by 1.5 mAP: https://t.co/ysSH1iRs5n

Massive thanks to our community contributor @Lezwon #ComputerVision #MachineLearning

[Vasilis Vryniotis @bbriniotis]

Here is my latest blog post on @PyTorch's journey to Modernize TorchVision: https://t.co/8M25vZ65OJ
#MachineLearning #ComputerVision #DataScience

[Vasilis Vryniotis @bbriniotis]

The Swin Transformer model was just merged in TorchVision, along with pre-trained weights for swin_t (Acc@1 81.4%). More variants coming soon!

Massive thanks to Hu Ye for fully reproducing the results of the paper: https://t.co/pt9cZEHEnr #PyTorch #DataScience #ComputerVision

[Vasilis Vryniotis @bbriniotis]

TorchVision refreshed its most popular Detection & Segmentation models to improve their accuracies:
- RetinaNet: 41.5 mAP (+5.1)
- FasterRCNN: 46.7 mAP (+9.7)
- MaskRCNN: 47.4 box mAP (+9.5) / 41.8 mask mAP (+7.2)
https://t.co/s7MWtWibEG
#MachineLearning #ComputerVision #PyTorch

[Vasilis Vryniotis @bbriniotis]

Say hello to TorchVision's new Multi-weight support API!!

It's fully-BC, enables support of multiple pre-trained weights for the same model variant, bundles together the preprocessing transforms & other crucial meta-data: https://t.co/VV2Cv6yrfR
#MachineLearning #ComputerVision

[Vasilis Vryniotis @bbriniotis]

The EfficientNetV2 architecture was just merged in TorchVision. We offer pretained weights on ImageNet with the following Acc@1:
- Small 84.2%
- Medium 85.1%
- Large 85.8%
https://t.co/mhe3ukzhGy #PyTorch #DataScience #ComputerVision

[Vasilis Vryniotis @bbriniotis]

We just merged the AugMix auto-augmentation strategy to TorchVision. Many thanks to @TheNormanMu and @hendrycks for their help & reviews: https://t.co/fUeXOuR0QF

We also added the Large Scale Jitter transform for Object Detection:
https://t.co/eHab5WgqIn
#PyTorch #DataScience

[Vasilis Vryniotis @bbriniotis]

The ConvNeXt architecture has graduated from prototype into main TorchVision! We offer improved pre-trained weights, using our latest training recipe, with the following Acc@1:
- Tiny 82.5%
- Small 83.6%
- Base 84.1%
- Large 84.4%
https://t.co/fWkq09yAOP #PyTorch #ComputerVision

[SovitRath5 @SovitRath5]

https://t.co/L2p6fdwuv1
New post at DebuggerCafe - PyTorch EfficientNetB0 for Brain MRI Image Classification
#PyTorch #EfficientNet #EfficientNetB0 #TransferLearning #FineTuning #ImageClassification #DeepLearning

[Vasilis Vryniotis @bbriniotis]

We just merged FCOS, the object detection architecture, along with pre-trained weights in TorchVision: https://t.co/qkfVirq0i6

Massive thanks to Hu Ye, @zhiq_w and Joao Gomes for their work on fully reproducing the results of the paper: https://t.co/NqbxwtNs0X

(1/3)

[Vasilis Vryniotis @bbriniotis]

I just merged to TorchVision a ResNet50 weights update contributed by @tbennun which pushed the accuracy to 80.9%: https://t.co/MWFTKtAnDr

This recipe+AdamW achieves 82.5% Acc@1 for the newly released ConvNeXt-T: https://t.co/SYJracdsF1

#PyTorch #ComputerVision #MachineLearning

[Vasilis Vryniotis @bbriniotis]

TorchVision introduces a new model API which offers multi-weight support. On the article below, we discuss the proposed changes & request for your feedback.

Help us finalize the API by providing your input:
https://t.co/yWFbfZ5hqW
#ComputerVision #MachineLearning #PyTorch