mlkit VS automl

I used ML Kit pos estimation API. I followed New features in ML Kit video and building use cases playlist. I tried to understand googlesamples/mlkit and tensorflow/examples but I barely understand, where the define the processor, and I loos it when it comes to the cameraSource file. eventually I created a runnable application.

To understand the implementation of getBitmap(), please refer to BitmapUtils.java.

Next, we draw an overlay above the camera preview. There is an excellent GraphicOverlay class provided by Android ML Kit demo project. We don't need to reinvent the wheel:

Download the sample code of ML Kit vision.

MLKit is distributed by CocoaPods, no Carthage or SMP support, but at least we ask Google to add such options...

I'm using Google Brain's EfficientDet repo to train an object detector. What hyperparameters should I choose to increase accuracy for small objects.

EfficientDet-D0 has comparable accuracy as YOLOv3.

Code for https://arxiv.org/abs/2104.00298 found: https://github.com/google/automl/efficientnetv2

7 Min Read | Paper (CoAtNet) | Paper (EfficientNetV2) | Google blog | Code

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.