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simple-faster-rcnn-pytorch
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automl | simple-faster-rcnn-pytorch | |
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7 | 1 | |
6,154 | 3,891 | |
0.7% | - | |
5.0 | 0.0 | |
24 days ago | almost 3 years ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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.
simple-faster-rcnn-pytorch
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ISO Easy to Modify and Use Faster RCNN PyTorch Implementation
Hi all, as the title suggests I'm looking for a GitHub repo where I can edit a Faster RCNN implementation rather easily. I'm basically looking to test an idea where I have multiple branches with feature map and bounding boxes as inputs. I've modified the built-in torchvision implementation once before, but I think it's a little more complicated than I like, and I'd rather not release the entire torchvision package as part of my own work in the future. I have looked briefly into this repo https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/master/trainer.py but it only supports a batch size of 1, and I'm not sure what it'd take to expand that capability. Is there anything better out there?
What are some alternatives?
gpt-3 - GPT-3: Language Models are Few-Shot Learners
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.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
SipMask - SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
OwnPhotos - Self hosted alternative to Google Photos
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
mlkit - A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS
opencv - Experimenting using Machine Vision OpenCV and Python to create software suitable for driving a Golf launch monitor similar to technology like SkyTrak, GC2 and GC Quad