obj-detection | Object detection demo based on yolov5 | Computer Vision library

by linjie98 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | obj-detection Summary

obj-detection is a Python library typically used in Artificial Intelligence, Computer Vision applications. obj-detection has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Object detection demo based on yolov5

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obj-detection has a low active ecosystem.

It has 19 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 12 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of obj-detection is current.

Quality

obj-detection has no bugs reported.

Security

obj-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

obj-detection does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

obj-detection releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed obj-detection and discovered the below as its top functions. This is intended to give you an instant insight into obj-detection implemented functionality, and help decide if they suit your requirements.

Generate frames from the source image
Computes the color for the given label
Calculate the relative coordinates of the bounding box
Draw boxes
Run detection
Compute the relative coordinates of the bounding box
Configure logging
Performs a non - suppression on a prediction
Compute the intersection between two boxes
Plot hyperparameters in evolution txt file
Download a file from Google Drive
R Check anchors in dataset
Schedules output by time
Print mutation results to evolve
Adds a bbox to the given frame
Cache dataset labels
Plot dataset labels
Calculate the cost of the cost of the detection
Plot images
Compute the loss for a given model
Parse the model dictionary
Compute precision recall curve
Train the network
Run test
Update the covariance matrix
Apply classification to images

Get all kandi verified functions for this library.

obj-detection Key Features

No Key Features are available at this moment for obj-detection.

obj-detection Examples and Code Snippets

No Code Snippets are available at this moment for obj-detection.

Community Discussions

Trending Discussions on obj-detection

How to solve "Variable is available in checkpoint, but has an incompatible shape with model variable"?

Error when storing checkpoints to Google Cloud bucket

QUESTION

How to solve "Variable is available in checkpoint, but has an incompatible shape with model variable"?

Asked 2019-Jul-01 at 16:58

I'm trying to retrain existing pretrained net from object-detection-API. It is ssd_mobilenet_v2. Pre-trained on COCO dataset. I was reproducing steps according to the tutorial pinned to obj-detection-API.

The model starts training anyway, but the % mAP is low. I'm new to CNN's at all, so any help is appreciated.

When I start training, then this warning appears and I can't find a fix.

I'm running it in a google-collaboratory notebook with this command

...

ANSWER

Answered 2019-Jan-08 at 17:23

Your error message says (taking the first line, they are all similar):

layer_19_2_Conv2d_2_3x3_s2_512/weights is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 512]], model variable shape: [[3, 3, 256, 512]].

The shape in the checkpoint, as interpreted per this question & answer, is that of a 1x1 convolution (the 1,1 at the beginning of the shape). The shape in your model is correctly the one of a 3x3 convolution. Now, this is weird because the layer name in the checkpoint has "3x3", although that would be wrong, given the weights shape.

It seems, then, you're using a checkpoint that used 1x1 convolutions for the layers you're having issues with, despite those layers having a name that implies being 3x3 convolutions. What you could try as a workaround to use the checkpoint you have is to amend the model modifying the function that builds it to use 1x1 convolutions instead (although I can't say for sure where that would be).

As per having a low %mAP, that is of course due to having part of the model reinitialized and not loaded properly.

Source https://stackoverflow.com/questions/54094981

QUESTION

Error when storing checkpoints to Google Cloud bucket

Asked 2018-Jul-06 at 04:30

I run a Tensorflow model with the ML Engine on Google Cloud, and the checkpoint saver fails to save files on the bucket. I am using TensorFlow 1.4, and tf.Estimator with the method tf.estimator.train_and_evaluate.

These are the log records, where gs://e-trial-central1/models/1530351907.8359423 is the argument model_dir given for the estimator:

...

ANSWER

Answered 2018-Jul-06 at 04:30

This was solved by moving to a more recent version of Tensorflow (1.8).

Source https://stackoverflow.com/questions/51117116

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install obj-detection

You can download it from GitHub.
You can use obj-detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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