Image Dataset For Object Detection

deer detected in an image with only 4 actual deer present, as an example [13]. It contains photos of litter taken under diverse environments, from tropical beaches to London streets. The shapes dataset has 500 128x128px jpeg images of random colored and sized circles, squares, and triangles on a random colored background. FreiHAND Dataset. edu Karthik Jagadeesh [email protected] Object segmentation by long term analysis of point trajectories T. Two commonly used databases are:. Pushy robots learn the fundamentals of object manipulation Systems 'learn' from novel dataset that captures how pushed objects move, to improve their physical interactions with new objects. For this tutorial, we will convert the SSD MobileNet V1 model trained on coco dataset for common object detection. So, in this post, we will learn how to train YOLOv3 on a custom dataset using the Darknet framework and also how to use the generated weights with OpenCV DNN module to make an object detector. Pascal VOC Dataset Mirror. TUD-Brussels: Dataset with image pairs recorded in an crowded urban setting with an onboard camera. Object Detection using Convolutional Neural Network (CNN) is presented. tral image dataset (60 images with respective salient object ground-truths) that can be used for salient object detection task. With the present contribution, a large-scale fully-labeled image dataset is provided, and made publicly and freely available to the research community. It consists of three test areas for which reference data for various object classes are. 3 of the dataset is out!. Given an input image, the segmentation task is to essentially determine for each pixel which object (or background) it belongs to, and the object detection task is to draw a bounding box around each object in the image and classify each object. Issue fixed I was having 2 python versions one was 2. [email protected] Jan 24, 2018. This dataset was used as training data of a classifier detecting a forklift pallet. We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. The dataset I made just contains copies of the same image and the corresponding label. We are interested in both the supervised and unsupervised scenarios. As hinted by the name, images in COCO dataset are taken from everyday scenes thus attaching “context” to the objects captured in the scenes. New models include: Segmentation Models. Overview of the Open Images Challenge 2018. But, it can not Rapid object detection using a boosted cascade of. These models are trained using a discriminative procedure that only requires bounding boxes for the objects in a set of images. The proposed method was applied to detection of forklift pallet. edu Sven Dickinson University of Toronto [email protected] It achieves state-of-the-art results on the RGB-D Object Dataset! December 13, 2012 - Software and data for detection-based object labeling in Kinect videos now available here. Object Detection with my dog. It achieves state-of-the-art results on the RGB-D Object Dataset! December 13, 2012 - Software and data for detection-based object labeling in Kinect videos now available here. "But applying it to plants is totally novel. However, the support for data augmentation for object detection tasks is still missing. Image Parsing. You could use them as such, if you just want to use it for standard object detection. So what’s the difference between Object Detection and Objet Recognition. detect 2 objects) Object Detection: Detect all available objects. COCO-Text: Dataset for Text Detection and Recognition. Please cite our paper if you use it. Introduction. Time was very limited. The random Poisson detection under weak light condition obtains partial information of the object. tral image dataset (60 images with respective salient object ground-truths) that can be used for salient object detection task. Using the integral image, face detection is completed before an image pyramid can be computed. 1 (a) and (b). All the code and dataset used in this article is available in my Github repo. Image classification versus object detection. The contribution of this paper is three-fold. Other details. There is the Landsat dataset, ESA’s Sentinel dataset, MODIS dataset, the NAIP dataset, etc. A small dataset is useful for exploring the Faster R-CNN training procedure, but in practice, more labeled images are needed to train a robust detector. Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Bekris and Alberto F. The COCO-Text V2 dataset is out. It also has binary mask annotations encoded in png of each of the shapes. To start with, I assume you know the basic knowledge of CNN and what is object detection. factory performance as applied to detect small objects in images. A range image dataset that consists of 62,400 positive and negative samples was made without manual pointing of the target pallet in range images. Click on the image to obtain an enlarged version: how many categories can you recognize from their average?. This is the link for original paper, named “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”. Let’s say we want to detect a person object in an image. Of course it’s not just image detection that a neural network can be trained to do: here is a video that shows how a car can be trained to race around a track using a very simple version of a. py (from object_detection/legacy). New data set could contain less images that is. IMAGE DATASETS. , Faster-RCNN or YOLO) and measure the. The Pascal VOC challenge is a very popular dataset for building and evaluating algorithms for image classification, object detection, and segmentation. Most objects are presented in a stereotypical pose. Here we define the 3D object detection task on nuScenes. ” While the distinguishable detail is dependent on the size of the image pixel, the size of an object that can be seen in an image and the size of a single pixel in an image are different. Methods that employ shared part models offer great promise toward scaling. Requirements:. 2m images, 30. In PASCAL3D+, we augment the 12 rigid categories in the PASCAL VOC 2012 dataset [4] with 3D annotations. It allows for the recognition, localization, and detection of multiple objects within an image which provides us with a much better understanding of an image as a whole. However, the support for data augmentation for object detection tasks is still missing. running the object classification and localization at ~67 ms per image. Later they add more conv layers and the FC layer responsible for detection. Section 5 discusses problematic issues. Example shape image and object masks. Home; People. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. AWS DeepLens Sample Projects Overview. They have substantially improved the performance of general supervised models, time series, speech recognition, sentiment analysis. The full dataset consists of 164,866 128×128 RGB-D images: 11 sessions × 50 objects × (around 300) frames per session. When you pass ML Kit images, ML Kit returns, for each image, a list of up to five detected objects and their position in the image. Training an R-CNN object detector from scratch using only 41 images is not practical and would not produce a reliable stop sign detector. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. It can even recognize many different kinds of actions, albeit not quite as well as. 7 and second was 3. SwRI has shown it is possible to orbit Pluto and then escape orbit to tour additional dwarf planets and Kuiper Belt Objects. Object Detection (Image) Popularity. This article collects a small daily object dataset, including the selection of 20 categories of items that are frequently touched in everyday life:. Classes are typically at the level of Make, Model, Year, e. record file use the code as shown below:. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). MakeML’s Automated Video Annotation Tool allows you to annotate objects in the first frame of a video, and then an object tracking algorithm will track an object in a video, and you, for example, can receive a dataset of a hundred annotated images from a short 4-second video in a couple of seconds. The objects that are then consistently identified on multiple frames as moving are kept. The dataset consists of 9 million images that have already been labelled by the team. In total we collected a relatively balanced dataset of approximately 17,000 images across these 7 brands and 1 negative class: Coach (1786) Gucci (1256) Prada (1533) Louis. You have a strong CNN classifier trained on ImageNet data set. ImageNet is a dataset of over 15 million labeled high-resolution images belonging to roughly 22,000 categories. Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. In Learning Transferable Architectures for Scalable Image Recognition, we apply AutoML to the ImageNet image classification and COCO object detection dataset -- two of the most respected large scale academic datasets in computer vision. Yesterday at Build 2018 a new Project Type was added to enable Object Detection in images. Preparing Image for model training. It can only predict the classes defined by the datasets. After the release of Tensorflow Lite on Nov 14th, 2017 which made it easy to develop and deploy Tensorflow models in mobile and embedded devices - in this blog we provide steps to a develop android applications which can detect custom objects using Tensorflow Object Detection API. However, doing the same augmentation for an object detection tasks also requires you to update the bounding box. These datasets can be indexed to return a tuple of an image, bounding boxes and labels. This might be. Abstract: We present Open Images V4, a dataset of 9. The tool provided the object coordinates in XML format as output for further. Annotating images and serializing the dataset. Some very large detection data sets, such as Pascal and COCO, exist already, but if you want to train a custom object detection class, you have to create and label your own data set. (Until they got a good top 5 error). As we originally conducted the experiments in the paper using the internal framework Brain++, this project is a reimplemented version on PyTorch. In this dataset, the focus is on vehicles with car, truck, and van object categories. I used Labellmg, which is a graphical image annotation tool that can be used to create labeled datasets. Object Detection. Computer Vision is the science and engineering that extracts useful information from images. Setup Tensorflow Object Detection API and 9. Figure 4: A screenshot of DIGITS showing how to create new datasets for object detection. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the. TL:DR; Open the Colab notebook and start exploring. For example, this. Issue fixed I was having 2 python versions one was 2. Object detection example. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image. Object Detection using Convolutional Neural Network (CNN) is presented. Hello, Darknet’s YOLO. The base CNN used in our model was a VGG16 network, which is a 16 layer deep CNN 38. Satellite Imagery Datasets. Run the script from the object_detection directory with arguments as shown here. COCO-Text: Dataset for Text Detection and Recognition. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image. LabelImg is a. Each image file should correspond to a single label file. Finally DeepLesion is a dataset of lesions on medical CT images. For example, an augmentation which horizontally flips the image for classification tasks will like look the one above. Various other datasets from the Oxford Visual Geometry group. The Group is looking to hire a machine learning \ Deep learning specialist to develop new approaches and capabilities to complement and enhance current capabilities, as well as come up with new innovative ones. Then, we tested our hyperspectral data with some spectral saliency models from [3] and [4]. For every scene, the dataset includes an RGB image, a depth map image, and correctly labeled bounding-box and segmentation data. The structure and approaches of these posts will follow the excellent fast. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). This dataset was used as training data of a classifier detecting a forklift pallet. the best algorithm for PASCAL VOC dataset, 1. ai notebook on object detection. (Keze Wang, Keyang Shi, Liang Lin, Chenglong Li ). Gathering a data set. Code snippets. October 3, 2012 - The dataset is now available for download directly from the website!. Real-Time Object Detection. involved, from scanned images to web pages, from plain texts to PDF files. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the. Download the TensorFlow models repository. The PASCAL Visual Object Classes Homepage The PASCAL VOC project: Provides standardised image data sets for object class recognition Provides a common set of tools for accessing the data sets and annotations; Enables evaluation and comparison of different methods. An image annotation tool to label images for bounding box object detection and segmentation. And i need the ''SAR ship dataset for detection, discrimination and analysis'' for my academic research. Consider the multi-class object detection problem. Each image may have several masks to indicate the presence of multiple objects. Content-based image retrieval is not yet a commercial success, because most real users searching for images want to specify the semantic class of the scene or the object(s) it should contain. Image Parsing. It contains 255 test images and features five diverse shape-based classes (apple logos, bottles, giraffes, mugs, and swans). Running the file from the base folder mean the paths will be relative to this folder, and the. The black hole is. To this end, we first present an extensive evaluation of standard local features for object detection on a large X-ray image dataset in a structured learning framework. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. The full dataset consists of 164,866 128×128 RGB-D images: 11 sessions × 50 objects × (around 300) frames per session. A critical aspect of this task corre-. Training image folder: The path to the location of the training images. When building datasets for machine learning object detection and recognition models, generating annotations for all of the images in the dataset can be very time consuming. The TensorFlow Object Detection API repository comes with Python scripts to train the model and run the prediction. py (from object_detection/legacy). It is similar to the MNIST dataset mentioned in this list, but has more labelled data (over 600,000 images). When detecting objects in video streams, every object has an ID that you can use to track the object across images. Deep Learning based methods to be covered in later posts. This binary mask format is fairly easy to understand and create. Here our goal is not only to say what is in the image but also to recognize where it is in the image. running the object classification and localization at ~67 ms per image. It includes code to run object detection and instance segmentation on arbitrary images. I used the Udacity’s openly available data-sets. In experiments, Faster R-CNN is used to test the proposed method on the PASCAL VOC 2007 and the COCO 2017 object detection datasets. Another project used video recorded during UAS flights and. Detected objects in a sample image (from the COCO dataset) made by one of our models. 3’s deep neural network ( dnn ) module. Gathering a data set. You can train a smaller model with supported configuration (MobileNet + SSD, input. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the. g, MS COCO or Pascal VOC) with N images where k object classes have been labeled. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image. You can use the Image Labeler app, Video Labeler app, or the Ground Truth Labeler app (requires Automated Driving Toolbox™). While it is related to classification, it is more specific in what it identifies, applying classification to distinct objects in an image/video and using bounding boxes to tells us where each object is in an image/video. COCO stands for Common Objects in Context. Image classification versus object detection. We use the filetrain. However, doing the same augmentation for an object detection tasks also requires you to update the bounding box. In general, if you want to classify an image into a certain category, you use image classification. Object detection Detect if an object is present and if present to what class of objects does it belong to. This dataset, produced by a group at Oxford University, includes image data for both segmentation and object detection tasks. For object detection in particular, we provide 15x more bounding boxes than the next largest datasets (15. There should be an interesting question that can be answered with the dataset. However, in aerial object detection, a dataset resembling MSCOCO and ImageNet both in terms of image number. COCO is a large-scale object detection, segmentation, and captioning dataset. For the longest. The final dataset prepared for training consists of 1,312 color images. The human annotations serve as ground truth for learning grouping cues as well as a benchmark for comparing different segmentation and boundary detection algorithms. Einstein Object Detection. Flexible Data Ingestion. Hence, the view of images are a little different from the drone-view images. Experiments We examined the performance of the proposed tracker on the DETRAC dataset [17] consisting of over 10 hours of video footage targeting vehicle detection and tracking. In this article I will show how anyone can train their own model for the purposes of Traffic Light Detection and Classification using the openly available data-sets and tools. Step 3: Creating an Object Detection Dataset with Distributed Model Interpretability. The datasets selected for the challenge were converted to a standard format. Look for datasets without too many rows and columns, because those are easier to work with. existing datasets and provide a challenging benchmark for 3D object detection and pose estimation. Finally DeepLesion is a dataset of lesions on medical CT images. A video dataset of spatio-temporally localized atomic visual actions, introduced in this paper. Install TensorFlow. Dataset 1 (37 MB) contains 30 omnidirectional images to detect (standing) humans (66 annotated instances) and 50 omnidirectional images to detect (side-view) cars (65 annotated instances). The research is described in detail in CVPR 2005 paper Histograms of Oriented Gradients for Human Detection and my PhD thesis. In our experiment, the Python* tool, LabelImg* 4 was used for annotation. ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. Before creating an LMDB dataset for the purposes of object detection, make sure that your training data resides on the shared file system. edu Abstract This paper addresses the problem of category-level 3D object detection. We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been paramount for successful 3D object detection algorithms, whereas monocular image-only methods experience drastically reduced performance. Object detection refers to the capability of computer and software systems to locate objects in an image/scene and identify each object. There were 1,743,042 images with 12,195,144 bounding boxes in total. In the second Cityscapes task we focus on simultaneously detecting objects and segmenting them. Sliding Shapes for 3D Object Detection in Depth Images Abstract. As we originally conducted the experiments in the paper using the internal framework Brain++, this project is a reimplemented version on PyTorch. 1) A com-plete multi-stage framework is proposed for object detec-tion in videos. After your dataset has enough images with enough labeled objects, you can train a model. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of-the-art approaches for object recognition tasks. These datasets capture objects under fairly controlled conditions. Whitepaper on the dataset is on arXiv!. To reach acceptable “real-time” performance, the expectation is at least 15 fps (frames per second), i. To start with, I assume you know the basic knowledge of CNN and what is object detection. New models include: Segmentation Models. Peculiarities of this proposal are: Only requirement is the dataset created with LabelImg; A single Google Colab notebook contains all the steps: it starts from the dataset, executes the model's training and shows inference. It contains more than 14M images with 21841 synsets. I used Labellmg, which is a graphical image annotation tool that can be used to create labeled datasets. If you win, you get money, plus a bonus if you use MATLAB. According to their site, “The training set of V4 contains 14. The dataset for spatio-temporal action detection, introduced in "Towards Weakly-Supervised Action Localization" (arXiv), is available here. The resulting system is both efficient and accurate, achieving state-of-. Schmid "From Images to Shape Models for Object Detection", International Journal of Computer Vision (IJCV), 2009. jpg images named JPEGImages and one for annotations named Annotations. A versatile, automatic image annotation and dataset management platform. TensorFlow also provides pre-trained models, trained on the MS COCO, Kitti, or the Open Images datasets. However, in aerial object detection, a dataset resembling MSCOCO and ImageNet both in terms of image number. Here is a subset of the pet image data set that I collected in images folder: Afterward, labeled them manually with LabelImg. In context of our example, this dataset is sure not to contain any examples of cars. OTCBVS Benchmark Dataset Collection OTCBVS. In this track, the goal is to develop a method of unsupervised domain adaptation for object classification, where the target domains contain images of additional unknown categories not present in the source dataset. edu Raquel Urtasun TTI Chicago [email protected] Databases or Datasets for Computer Vision Applications and Testing. Four important computer vision tasks are classification, localization, object detection and instance segmentation (image taken from cs224d course):. TensorFlow’s Object Detection API at work. Run the script from the object_detection directory with arguments as shown here. It contains 255 test images and features five diverse shape-based classes (apple logos, bottles, giraffes, mugs, and swans). – Feeding raw image pixels to the networks is difficult to detect saliency, as saliency does not depend on particular object appearance (e. Another project used video recorded during UAS flights and. There is one additional image per placemark which shows some overlaid markers, such as the address, name of streets, etc. In this post, I'll discuss an overview of deep learning techniques for object detection using convolutional neural networks. intro: ECCV 2018 from RGB-D Images for Object Detection and. and this dataset contains around 330K labeled images. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, […] and (2) object-level annotation of a tight bounding box and class label around an object instance in the image — ImageNet Large Scale Visual Recognition Challenge, 2015. For example, this. Cars Dataset; Overview The Cars dataset contains 16,185 images of 196 classes of cars. To get started with AWS DeepLens, use the sample project templates. 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model Sanja Fidler TTI Chicago [email protected] For example, in my case it will be "nodules". Bigbird is the most advanced in terms of quality of image data and camera poses, while the RGB-D object dataset is the most extensive. People often confuse image classification and object detection scenarios. record file use the code as shown below:. For example, an augmentation which horizontally flips the image for classification tasks will like look the one above. All images are color and saved as png. PASCAL: Static object dataset with diverse object views and poses. Most images have little or no clutter. as_default(): with tf. record and 10% test. Consider the multi-class object detection problem. end object detection systems based on deep learning. The test images were captured in scenes with graded complexity, often with clutter and occlusion. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not. Tensorflow’s Object Detection API. These datasets capture objects under fairly controlled conditions. Training Data for Object Detection and Semantic Segmentation. Gathering a data set. COCO-Text: Dataset for Text Detection and Recognition. Benchmarks for Recognizing Human-Object Interactions in Images Learning to Detect Human-Object Interactions. After detecting an object, localize its position within the image. As we originally conducted the experiments in the paper using the internal framework Brain++, this project is a reimplemented version on PyTorch. It can only predict the classes defined by the datasets. Objects Obtained With fLight (OOWL) The OOWL dataset is a real-world multiview dataset collected with drones, enabling flexibility and scalability. Last updated 2 September 2005. Object detection is the ability to detect the presence and location of specific objects within a frame. Object Detection (Image) Popularity. Each year, multiple teams submit AI algorithms to try to obtain the highest accuracy on object detection and classification tasks from image data — capabilities of great interest to the Department of Defense. Object detection has been applied widely in video surveillance, self. Pascal VOC Dataset Mirror. record) - dataset_to_tfrecord. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and. It contains 255 test images and features five diverse shape-based classes (apple logos, bottles, giraffes, mugs, and swans). Hi, Here is a great compilation of open vehicle datasets: 250,000 Cars - Top 10 Free Vehicle Image and Video Datasets for Machine Learning Some of the datasets will be annotated with bounding boxes, but some of the datasets include images only wit. We address each in turn. At this point, we have shown how to create a deep image classification system that leverages Bing to eliminate the need for labelled data. The model implementations provided include RetinaNet, YOLOv3 and TinyYOLOv3. towardsdatascience. "The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale", Kuznetsova et al 2018 {Google} [9. Specifically, this relates to research on detecting brake lights for autonomous vehicles. For example, in my case it will be “nodules”. Object segmentation by long term analysis of point trajectories T. The datasets selected for the challenge were converted to a standard format. Run inference. The following post is from Neha Goel, Champion of student competitions and online data science competitions. running the object classification and localization at ~67 ms per image. Methods that employ shared part models offer great promise toward scaling. Antonio Torralba averaged the images of each category producing this composite image. The whole period of the competition was less than 2 months. The driving scenario increases the number and heterogeneity of the observed object classes. And the other is small objects; those are large objects in the real world, but they are shown in the image as small objects because of the camera angle and focal length, such as objects detection in aerial images or in remote sensing images. You could use them as such, if you just want to use it for standard object detection. The training images show individual objects from different viewpoints and were either captured by a Kinect-like sensor or obtained by rendering of the 3D object models. Pascal VOC Dataset Mirror. Object Detection. Great! We now have a. in both object classi cation and detection research using a new class of deep learning algorithms [5,6,7]. Today we're announcing the availability of our newest Einstein Platform Services offering - Einstein Object Detection in beta. txt file per image in the training set, telling YOLOv2 where the object we want to detect is at: our data set is completely annotated. While it is related to classification, it is more specific in what it identifies, applying classification to distinct objects in an image/video and using bounding boxes to tells us where each object is in an image/video. Face detection isn't a type of motion detection so doesn't fit here - it's available on the Alerts tab. Each subject is shown randomly a subset of the Berkeley segmentation dataset as boundaries overlapped on the corresponding images. To start with, I assume you know the basic knowledge of CNN and what is object detection. Since such a dataset does not currently exist, in this study we generated our own multispectral dataset. The structure and approaches of these posts will follow the excellent fast. Jurie, and C. The datasets selected for the challenge were converted to a standard format. Hence, the view of images are a little different from the drone-view images. The data collection followed the basic guidelines provided at here. The winner of the detection challenge will be the team which achieves first place accuracy on the most object categories. The training images show individual objects from different viewpoints and were either captured by a Kinect-like sensor or obtained by rendering of the 3D object models. You could use them as such, if you just want to use it for standard object detection. 6M bounding boxes for 600 object classes on 1. Based on this property, we demonstrated better performance of our method by enlarging the training dataset with multiple detections of the speckle patterns. COCO-Text: Dataset for Text Detection and Recognition. prepare images for training; generate training data for selected images by using VOOT tool, prepare Python code for object detection using FasterRCNN alogirithm implemented with CNTK, testing custom image in order to detect Nokia3310 on image. In the second Cityscapes task we focus on simultaneously detecting objects and segmenting them. The depth information of RGB-D sensors has greatly simplified some common challenges in computer vision and enabled breakthroughs for several tasks. LASIESTA: Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms LASIESTA is composed by many real indoor and outdoor sequences organized in diferent categories, each of one covering a specific challenge in moving object detection strategies. Image annotation.