TensorFlow's TFRecord Format

## Overview

TensorFlow's TFRecord Format is a dataset for object detection tasks - it contains Traffic Signs annotations for 219 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Overview

This dataset contains 196 images of raccoons and 213 bounding boxes (some images have two raccoons). This is a single class problem, and images vary in dimensions. It's a great first dataset for getting started with object detection.

This dataset was originally collected by Dat Tran, released with MIT license, and posted here with his permission.

https://i.imgur.com/cRQJ1PB.png" alt="Raccoon Example">

Per Roboflow's Dataset Health Check, here's how images vary in size:

https://i.imgur.com/sXc3iAF.png" alt="Raccoon Aspect Ratio">

Use Cases

Find raccoons!

This dataset is a great starter dataset for building an object detection model. Dat has written a comprehensive tutorial here.

Getting Started

Fork or download this dataset and follow Dat's tutorial for more.

Context

The latest Google Landmark Retrieval competition contains a crazy large dataset (1.5 million images) and asks participants to only use notebooks. TPUs are a great way to quickly train models on large volumes of this data. To realise the full potential of a TPU while using Tensorflow it is worth feeding the data into it as tfrecords.

Content

This dataset contains a sample of the total dataset but transformed into tfrecords. As I created this for use with a model that uses triplet loss you will find three images inside each example (i.e. a triplet). If you'd like to find out more about how the dataset is formed you can check out the notebook I used to create it here.

Acknowledgements

The notebook I used to create this dataset was largely inspired by Chris Deottes notebook so this is me saying thanks 😁.

CIFAR-10

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. * More info on CIFAR-10: https://www.cs.toronto.edu/~kriz/cifar.html * TensorFlow listing of the dataset: https://www.tensorflow.org/datasets/catalog/cifar10 * GitHub repo for converting CIFAR-10 tarball files to png format: https://github.com/knjcode/cifar2png

All images were sized 32x32 in the original dataset

The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images [in the original dataset].

The dataset is divided into five training batches and one test batch, each with 10,000 images. The test batch contains exactly 1,000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5,000 images from each class.

Here are the classes in the dataset, as well as 10 random images from each: https://i.imgur.com/EGA4Bbf.png" alt="Visualized CIFAR-10 Dataset Subset">

The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. Automobile includes sedans, SUVs, things of that sort. Truck includes only big trucks. Neither includes pickup trucks.

Version 1 (original-images_Original-CIFAR10-Splits):

• Original images, with the original splits for CIFAR-10: train (83.33% of images - 50,000 images) set and test (16.67% of images - 10,000 images) set only.
• This version was not trained

Version 3 (original-images_trainSetSplitBy80_20):

• Original, raw images, with the train set split to provide 80% of its images to the training set (approximately 40,000 images) and 20% of its images to the validation set (approximately 10,000 images)
• https://blog.roboflow.com/train-test-split/ https://i.imgur.com/kSPeKGn.png" alt="Train/Valid/Test Split Rebalancing">

Citation:

@TECHREPORT{Krizhevsky09learningmultiple,
  author = {Alex Krizhevsky},
  title = {Learning multiple layers of features from tiny images},
  institution = {},
  year = {2009}
}

Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthqu..., The raw seismic waveforms (.sac files) were recorded by the Geophones and DATA-CUBE (https://digos.eu/wp-content/uploads/2020/11/2020-10-21-Broschure.pdf) and converted to mseed format with cub2mseed command (https://digos.eu/CUBE/DATA-CUBE-Download-Data-2017-06.pdf) of the CubeTools utility package (https://digos.eu/seismology/). The .tfrecord files are generated using the scripts host on Github and a permanent identifier to Zenodo., Please clone the RockNet project on Github (https://github.com/tso1257771/RockNet) and put the downloaded dataset under the cloned directory. *The SAC software (Seismic Analysis Code, http://ds.iris.edu/ds/nodes/dmc/software/downloads/sac/102-0/) is used to process and visualize SAC files. *The ObsPy (https://docs.obspy.org/) package is used to process and manipulate SAC files in the python interface. *The h5py package (https://docs.h5py.org/en/stable/) is used to store seismic data and header information (i.e., metadata, including station and labeled information) in HDF5 (https://hdfgroup.org/) format for broader usages. *The ObsPy and TensorFlow packages (https://www.tensorflow.org/) are collaboratively used to convert the SAC files into the TFRecord format (https://www.tensorflow.org/tutorials/load_data/tfrecord) for TensorFlow applications.

CIFAR-100

The CIFAR-10 and CIFAR-100 dataset contains labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. * More info on CIFAR-100: https://www.cs.toronto.edu/~kriz/cifar.html * TensorFlow listing of the dataset: https://www.tensorflow.org/datasets/catalog/cifar100 * GitHub repo for converting CIFAR-100 tarball files to png format: https://github.com/knjcode/cifar2png

All images were sized 32x32 in the original dataset

The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images [in the original dataset].

This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). However, this project does not contain the superclasses. * Superclasses version: https://universe.roboflow.com/popular-benchmarks/cifar100-with-superclasses/

More background on the dataset: https://i.imgur.com/5w8A0Vm.png" alt="CIFAR-100 Dataset Classes and Superclassees">

Version 1 (original-images_Original-CIFAR100-Splits):

• Original images, with the original splits for CIFAR-100: train (83.33% of images - 50,000 images) set and test (16.67% of images - 10,000 images) set only.
• This version was not trained

Version 2 (original-images_trainSetSplitBy80_20):

• Original, raw images, with the train set split to provide 80% of its images to the training set (approximately 40,000 images) and 20% of its images to the validation set (approximately 10,000 images)
• Trained from Roboflow Classification Model's ImageNet training checkpoint
• https://blog.roboflow.com/train-test-split/ https://i.imgur.com/kSPeKGn.png" alt="Train/Valid/Test Split Rebalancing">

Citation:

@TECHREPORT{Krizhevsky09learningmultiple,
  author = {Alex Krizhevsky},
  title = {Learning multiple layers of features from tiny images},
  institution = {},
  year = {2009}
}

Overview

The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.

We re-labeled the dataset to correct errors and omissions. We have provided convenient downloads in many formats including VOC XML, COCO JSON, Tensorflow Object Detection TFRecords, and more.

Some examples of labels missing from the original dataset: https://i.imgur.com/A5J3qSt.jpg" alt="Examples of Missing Labels">

Stats

The dataset contains 97,942 labels across 11 classes and 15,000 images. There are 1,720 null examples (images with no labels).

All images are 1920x1200 (download size ~3.1 GB). We have also provided a version downsampled to 512x512 (download size ~580 MB) that is suitable for most common machine learning models (including YOLO v3, Mask R-CNN, SSD, and mobilenet).

Annotations have been hand-checked for accuracy by Roboflow.

https://i.imgur.com/bOFkueI.pnghttps://" alt="Class Balance">

Annotation Distribution: https://i.imgur.com/NwcrQKK.png" alt="Annotation Heatmap">

Use Cases

Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.

Using this Dataset

Our updates to the dataset are released under the MIT License (the same license as the original annotations and images).

Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Project Name: " Weed detection using ESP32" Project overview: The target is to develop a model to detect weeds in the field and so that can easily be detected and detached.

Descriptions: We will use ESP32 which has a camera and real time image can be seen with it. We will train the model with tensorflow and than run the algorithm in the ESP32. Then based on the algorithm weeds can be detected from the field.

Links to external resources: https://universe.roboflow.com/roboflow-100/grass-weeds/dataset/2

Overview

The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.

We re-labeled the dataset to correct errors and omissions. We have provided convenient downloads in many formats including VOC XML, COCO JSON, Tensorflow Object Detection TFRecords, and more.

Some examples of labels missing from the original dataset: https://i.imgur.com/A5J3qSt.jpg" alt="Examples of Missing Labels">

Use Cases

Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.

Using this Dataset

Our updates to the dataset are released under the same license as the original.

Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer: