automl
SipMask
automl | SipMask | |
---|---|---|
7 | 1 | |
6,162 | 333 | |
0.5% | - | |
5.0 | 0.0 | |
about 2 months ago | about 3 years ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
automl
- Slowdown / normalization on the Front Lines
- Lion, a new Optimizer from Google, provides 3-5x speedup compared to AdamW
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How do I increase the accuracy of small objects when training an object detector?
I'm using Google Brain's EfficientDet repo to train an object detector. What hyperparameters should I choose to increase accuracy for small objects.
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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.
SipMask
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What frameworks/libraries are suited for live instance segmentation?
SipMask is a fast instance segmentation in Pytorch.
What are some alternatives?
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
Yet-Another-EfficientDet-Pytorch - The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Vision-Project-Image-Segmentation
gpt-3 - GPT-3: Language Models are Few-Shot Learners
cellpose - a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
multi-object-tracking-in-python - 📡 implementation of multi object tracking algorithms including PMBM (Poisson Multi Bernoulli Mixture filter) in Python 🐍
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
mlkit - A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS
LightAutoML - LAMA - automatic model creation framework
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.