Automl Alternatives
Similar projects and alternatives to automl
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simple-faster-rcnn-pytorch
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TFLiteClassification
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SipMask
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
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H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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mlkit
A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS
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SonarLint
Deliver Cleaner and Safer Code - Right in Your IDE of Choice!. SonarLint is a free and open source IDE extension that identifies and catches bugs and vulnerabilities as you code, directly in the IDE. Install from your favorite IDE marketplace today.
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automl reviews and mentions
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Android QR Code Detection with TensorFlow Lite
EfficientDet-D0 has comparable accuracy as YOLOv3.
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[R] Google AI Introduces Two New Families of Neural Networks Called ‘EfficientNetV2’ and ‘CoAtNet’ For Image Recognition
Code for https://arxiv.org/abs/2104.00298 found: https://github.com/google/automl/efficientnetv2
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Google AI Introduces Two New Families of Neural Networks Called ‘EfficientNetV2’ and ‘CoAtNet’ For Image Recognition
7 Min Read | Paper (CoAtNet) | Paper (EfficientNetV2) | Google blog | Code
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[R] EfficientNetV2: Smaller Models and Faster Training
Abstract: This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of- the-art models while being up to 6.8x smaller. > Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. > With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at this https URL.
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google/automl is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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