Yet-Another-EfficientDet-Pytorch | official efficientdet with SOTA performance | Computer Vision library
kandi X-RAY | Yet-Another-EfficientDet-Pytorch Summary
kandi X-RAY | Yet-Another-EfficientDet-Pytorch Summary
The pytorch re-implement of the official EfficientDet with SOTA performance in real time, original paper link:
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Top functions reviewed by kandi - BETA
- Train the model
- Execute replication callbacks
- Replicate the slave
- Patch the replication callback
- Compute the loss function
- Postprocessing
- Calculates the IOU curve
- Return the result of the operation
- Evaluate a coco model
- Invert affine transformation
- Pre - process images
- Preprocess images
- Preprocess a video
- Create a pretrained model from pretrained model
- Convert a list of color names to bgr
- Encode a list of blocks
- Reset parameters
- Perform forward computation
- Evaluate COCO evaluation
- Invert affine
- Postprocessing post processing
- Perform the Convolution layer
- Forward computation
- Get command line arguments
- Performs the forward computation
- Calculates the mean standard deviation from a list of intermediates
- Display one or more images
- Get image size
Yet-Another-EfficientDet-Pytorch Key Features
Yet-Another-EfficientDet-Pytorch Examples and Code Snippets
Lines of Code : 83License : Permissive (MIT)
Copyuse Umbrella\YaBoleto\Builder\BoletoBuilder;
use Umbrella\YaBoleto\Endereco;
use Umbrella\YaBoleto\Cnpj;
use Umbrella\YaBoleto\Cpf;
// sacado...
$nomeSacado = "John Doe";
$documentoSacado = new Cpf("090.076.684-04");
$enderecoSacado = new Ender $ ./yabf shell basic
YABF Command Line Client
Type "help" for command line help
Connected.
> help
Commands
read key [field1 field2 ...] - Read a record
scan key recordcount [field1 field2 ...] - Scan starting at key
insert key name1=value1 [ usage: yand_cli.py [-h] [-V] [-l LOGFILE] [-C] [-f FILE] [-r] [-w] [-e]
[--write_value WRITE_VALUE] [--write_pgm] [--start START]
[--end END] [-P PAGE_SIZE] [-B PAGES_PER_BLOCK]
[-K NUMBER_OF_B Community Discussions
Trending Discussions on Yet-Another-EfficientDet-Pytorch
QUESTION
I have been checking out this detr repository and the total number of classes are 100, but 10 of these are empty string as shown here.
Is there any particular reason behind this?
ANSWER
Answered 2020-Dec-17 at 13:18Basically, the COCO dataset was described in a paper before its release (you can find it here). At this point, the authors gave a list of the 91 types of objects that would be in the dataset.
But when the 2014 and 2017 datasets sere released, it turned out that you could find only 80 of these objects in the annotations.
The list you have is the original list of objects (as described in the paper) but with every object that does not appear in the 2014 and 2017 replaced by the empty string "".
My guess is that the sole purpose of keeping these "phantom" objects is to keep consistency with object ids that may have been fixed someday in the past.
If you want to learn more about it, you can look at this blog entry.
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