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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: lnkd.in/eSXpFuVU #ComputerVision #MachineLearning #PyTorch

I just merged to TorchVision a ResNet50 weights update contributed by Tal Ben-Nun which pushed the accuracy to 80.9%: github.com/pytorch/vision… This recipe+AdamW achieves 82.5% Acc@1 for the newly released ConvNeXt-T: github.com/pytorch/vision… #PyTorch #ComputerVision #MachineLearning

We just merged FCOS, the object detection architecture, along with pre-trained weights in TorchVision: github.com/pytorch/vision… Massive thanks to Hu Ye, @zhiq_w and Joao Gomes for their work on fully reproducing the results of the paper: arxiv.org/abs/1904.01355 (1/3)

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% github.com/pytorch/vision… #PyTorch #ComputerVision

We just merged the AugMix auto-augmentation strategy to TorchVision. Many thanks to Norman Mu and Dan Hendrycks for their help & reviews: github.com/pytorch/vision… We also added the Large Scale Jitter transform for Object Detection: github.com/pytorch/vision… #PyTorch #DataScience

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% github.com/pytorch/vision… #PyTorch #DataScience #ComputerVision

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: github.com/pytorch/vision… #MachineLearning #ComputerVision

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) github.com/pytorch/vision… #MachineLearning #ComputerVision #PyTorch

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: github.com/pytorch/vision… #PyTorch #DataScience #ComputerVision

Here is my latest blog post on PyTorch's journey to Modernize TorchVision: blog.datumbox.com/the-journey-of… #MachineLearning #ComputerVision #DataScience

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: github.com/pytorch/vision… Massive thanks to our community contributor Lezwon Castelino #ComputerVision #MachineLearning

We have just merged the MViT architecture in TorchVision. This transformer video model achieves an 78.5% accuracy on Kinetics400. github.com/pytorch/vision… Image variants to be released soon. Many thanks to the PyTorch Video team for their support! #ComputerVision #MachineLearning

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 pytorch.org/blog/pytorch-1…

Natural Language Processing with Groovy, OpenNLP, CoreNLP, Nlp4j, Datumbox, Smile, Spark NLP, DJL and TensorFlow blogs.apache.org/groovy/entry/n… #groovylang Apache Spark ApacheGroovy djl TensorFlow Apache OpenNLP Stanford NLP Group Haifeng Li Datumbox oyahiroki 😝❤🦑🐙💻🤖ʬ ｫ ｫ ｬ

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: github.com/pytorch/vision… github.com/pytorch/vision…

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: bit.ly/3QykfD5

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: blog.datumbox.com/vernamveil-a-f…