automl
efficientdet-pytorch
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automl | efficientdet-pytorch | |
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7 | 1 | |
6,154 | 1,550 | |
0.7% | - | |
5.0 | 4.1 | |
24 days ago | 9 months ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
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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.
efficientdet-pytorch
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Bounding box annotations and object orientation
However, there are papers on oriented object detectors (see https://arxiv.org/pdf/1911.07732.pdf) for example. In that paper, they do achieve better results using oriented bounding boxes. If you want to go down that route, I would suggest using the EfficientDet model, because the PyTorch code that you'll find for it is quite easy to understand and modify. For example, I've taken https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch, and modified it to include a "thing-ness" logit, and this was pretty easy to do. Classic EfficientDet models only include logits (aka output neurons that get softmax-ed) for each class, and if any one of these class neurons is greater than 0.5, then it is considered "a thing". Anyway - that's digression, but my point is that I've thought about adding oriented box support to an EfficientDet model, and it didn't seem to be too hard, although I haven't actually done it. If I was to start now, I would probably go with https://github.com/rwightman/efficientdet-pytorch, since Ross Wightman's models are becoming a de-facto standard in the PyTorch world for all things image-related.
What are some alternatives?
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
gpt-3 - GPT-3: Language Models are Few-Shot Learners
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Yet-Another-EfficientDet-Pytorch - The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
SipMask - SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
mlkit - A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS
involution - [CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator