Thanks. Software is based on modern technologies based on neural networks, trained on large data sets. This metric can be evaluated independently of the algorithm/model used. In this article, we will go over all the steps needed to create our object detector from gathering the data all the way to testing our newly created object detector. While most videos are filmed with fixed-position CCTV … 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 … The steps needed are: 1. Image data. It allows for object detection at different scales by stacking multiple convolutional layers. Roboflow hosts free public computer vision datasets in many popular formats (including CreateML JSON, COCO JSON, Pascal VOC XML, YOLO v3, … Figure 2: The raccoon object detection dataset is curated by Dat Tran. We have taken a random whatsapp video in this task. Object detectionmethods try to find the best bounding boxes around objects in images and videos. You can see the result here . "Detect or Track: Towards Cost-Effective Video Object Detection/Tracking". Text detection ground truth on the Grocery Products dataset and the Glass Video dataset (frames are ... Pixel-level object masks for a subset of the YouTube-Objects video dataset. Chosen by, "http://com.dataturks.a96-i23.open.s3.amazonaws.com/2c9fafb0646e9cf9016473f1a561002a/77d1f81a-bee6-487c-aff2-0efa31a9925c____bd7f7862-d727-11e7-ad30-e18a56154311.jpg", gdown --id 1mTtB8GTWs74Yeqm0KMExGJZh1eDbzUlT --output indian_number_plates.json, 'https://github.com/fizyr/keras-retinanet/releases/download/0.5.1/resnet50_coco_best_v2.1.0.h5', Focal Loss for Dense Object Detection (2017), Object detection: speed and accuracy comparison, Object Detection in Colab with Fizyr Retinanet, the confidence score (0 to 1) for each bounding box and label. There is, however, some overlap between these two scenarios. TL;DR Learn how to prepare a custom dataset for object detection and detect vehicle plates. THP: Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei. Each video is about 40 seconds long, 720p, and 30 fps. On the other hand, if you aim to identify the location of objects in an image, and, for example, count the number of instances of an object, you can use object detection. Gathering data 2. Sea Animals Video Dat… Until a few years ago, computer vision only worked with limited capacity. To build TrackingNet, the researchers filtered out 90% of the videos by selecting the videos that a) are longer than 15 seconds; b) include bounding boxes that cover less than 50% of … There are about 200 images for each class and all images include an annotation for the species and breed name, a bounding box around the animal’s head, and a pixel-level segmentation of the foreground and background of the image. I have experience of working with Machine learning, Deep learning real-time problems, Neural networks, structuring and machine learning projects. Download Mask RCNN Coco Weights Instance Segmentation and Detection from Video Output If you like this notebook please upvote. Go from prototyping to deployment with PyTorch and Python! Configuring training 5. Required libraries. It costs 2.99$ per month or 29.99$ per year, but it has a free trial that lasts one week, so it will be enough to create and export your first object detection dataset. To address this issue, we elaborately collected a visual-attention-consistent Densely Annotated VSOD (DAVSOD) dataset, which contains 226 videos … Detecting objects in images and video is a hot research topic and really useful in practice. For this reason, it has become a popular object detection model that we use with aerial and satellite imagery. 1. Learn how to solve real-world problems with Deep Learning models (NLP, Computer Vision, and Time Series). Object Detection software turns your computer into a powerful video-security system, allowing you to watch what's going on in your home or business remotely. The Cambridge-driving Labeled Video Database (CamVid) is the first collection of videos with object class semantic labels, complete with metadata. beginner , deep learning , cnn , +1 more computer vision 450 Object detection is one of the key functions in autonomous driving. Let’s start with downloading the JSON file: We can use Pandas to read the JSON into a DataFrame: Next, we’ll download the images in a directory and create an annotation file for our training data in the format (expected by Keras RetinaNet): We can unify the download and the creation of annotation file like so: We can use the dict to create a Pandas DataFrame: Let’s get a look at some images of vehicle plates: We’ve already done a fair bit of preprocessing. Supervoxel-Consistent Foreground Propagation in Video. We can get the average precision for a single class by computing the IoU for every example in the class and divide by the number of class examples. In the Data annotation tool, we will label the objects in the image. A lot of classical approaches have tried to find fast and accurate solutions to the problem. However, well maintained, bug-free, and easy to use implementation of a good-enough model can give you a good estimate of how well you can solve your problem. Copyright Analytics India Magazine Pvt Ltd, Government May Soon Launch Data Centre Policy With Favourable Guidelines, Top 8 Data Visualisation Libraries In JavaScript, Hands-on Guide to OpenAI’s CLIP – Connecting Text To Images, Microsoft Releases Unadversarial Examples: Designing Objects for Robust Vision – A Complete Hands-On Guide, Tech Behind Facebook AI’s Latest Technique To Train Computer Vision Models. It assigns more weight on hard, easily misclassified examples and small weight to easier ones. RetinaNet is not a SOTA model for object detection. we ensure that each video contains at least one object of the corresponding class. We will use the dataset to perform R-CNN object detection with Keras, TensorFlow, and Deep Learning. Generating TFRecords for training 4. In this post, we showcase how to train a custom model to detect a single object using Amazon Rekognition Custom Labels. This guide shows you how to fine-tune a pre-trained Neural Network on a large Object Detection dataset. Your Datasets Dataset Type. Deep Learning ch… Not by a long shot. RetinaNet is built on top of two crucial concepts - Focal Loss and Featurized Image Pyramid: Focal Loss is designed to mitigate the issue of extreme imbalance between background and foreground with objects of interest. In general, if you want to classify an image into a certain category, you use image classification. 29.11.2019 — Deep Learning, Keras, TensorFlow, Computer Vision, Python — 6 min read. The next helper function will draw the detected boxes on top of the vehicle image: We’ll draw detections with a confidence score above 0.6. Run the complete notebook in your browser. As you can see from above fig, in the top left we can see annotation column by clicking on export option we can download our annotated data. The training script requires paths to the annotation, classes files, and the downloaded weights (along with other options): Make sure to choose an appropriate batch size, depending on your GPU. The best performing algorithms usually consider these two: COCO detection dataset and the ImageNet classification dataset for video object recognition. Those methods were slow, error-prone, and not able to handle object scales very well. Use transfer learning to finetune the model and make predictions on test images. We release individual video frames after decompression and after shot partitioning. Sliding windows for object localization and image pyramids for detection at different scales are one of the most used ones. KITTI 2D object detection dataset is a popular dataset primarily designed for autonomous driving, which contains 7481 … Creating the darkflow model instance … Then we need to add the list of objects we need to annotate and we can use the same list of objects for both object detection and segmentation tasks as shown in the below image. Go and have a look at the Readme to get a feel of what is capable of. Public datasets. Detect objects in varied and complex images. In simple terms, computer vision enables our computer to process and visualize the data. If you want to classify an image into a certain category, it could happen that the object or the characteristics that ar… Go get a hot cup of rakia, while waiting. cap = cv2.VideoCapture('/content/WhatsApp Video 2020-07-28 at 9.02.25 AM.mp4'), name = './data/frame' + str(currentFrame) + '.jpg'. Then, we will have a look at the first program of an HDevelop example series on object detection. It has a wide array of practical applications - face recognition, surveillance, tracking objects, and more. Abstract The last decade has witnessed a growing interest in video salient object detection (VSOD). If you use this dataset, please cite and . Comprehensive Guide To 9 Most Important Image Datasets For Data Scientists, Google Releases 3D Object Detection Dataset: Complete Guide To Objectron (With Implementation In Python), Machine Learning Developers Summit 2021 | 11-13th Feb |. You’ve built an Object Detector that can (somewhat) find vehicle number plates in images. The weapon detection task can be performed through different approaches that determine the type of required images. Dataset Type #Videos Annotation Annotation Type Year Paper Comments {{competition.datasetTitle}} {{competition.datasetDescription}} {{competition.type}} Useful to train or evaluate video foreground segmentation algorithms. If you use our dataset, please cite the following paper: Object detection deals with detecting instances of a certain class, like inside a certain image or video. After drawing these regions, we can download the data in either CSV format, JSON format, or COCO format. Third, the MOCS dataset is an image dataset and currently is focused on object detection. I am a Computer Vision researcher and I am Interested in solving real-time computer vision problems. In the region shape, we use a polyline for labeling segmentation data because using a rectangle bounding box we can’t draw bounding boxes in considering each pixel. This book will guide you on your journey to deeper Machine Learning understanding by developing algorithms in Python from scratch! A lot of classical approaches have tried to find fast and accurate solutions to the problem. A value close to 1 indicates a very good overlap while getting closer to 0 gives you almost no overlap. The task we’re going to work on is vehicle number plate detection from raw images. Training model 6. S. Jain and K. Grauman. Afterwards we will split this dataset and preprocess the labeled data to be suitable for the deep learning model. The database addresses the need for experimental data to quantitatively evaluate emerging algorithms. Power BI is a business analytics service provided by Microsoft. The videos are weakly annotated, i.e. There are tons of data around us but there is a very little amount of labelled data. Can you use the concepts you learned here and apply it to a problem/dataset you have? Next, we need to add an additional dimension to the image tensor, since the model works on multiple/batch of images. The duration of each video varies between 30 seconds and 3 minutes. Object Detection is the process of finding real-world object instances like car, bike, TV, flowers, and humans in still images or Videos. It has several features: Object segmentation; Recognition in context; Superpixel stuff segmentation; 330K images (>200K labeled) 1.5 million object instances; 80 object categories; 91 stuff categories; 5 captions per image; 250,000 people with keypoints; Size: ~25 GB (Compressed) Join the weekly newsletter on Data Science, Deep Learning and Machine Learning in your inbox, curated by me! You can try to run the detection with a lower threshold. Module 5 : Visualizing Object Detection Data in Near Real-Time with PowerBI. Thanks. The Tensorflow Object Detection API allows you to easily create or use an object detection model by making use of pretrained models and transfer learning. Modern object detectors are both fast and much more accurate (actually, usefully accurate). Firstly we fix the camera to the vehicle and we record the video while the vehicle is moving and we get a video file. Download free, open source datasets for computer vision machine learning models in a variety of formats. Through this article, we will demonstrate how to create our own image dataset from a video recording. Download the pre-trained model: Here, we save the weights of the pre-trained model on the Coco dataset. As suggested in the name, our dataset consists of 100,000 videos. The advancement in Computer Vision (CV) and Deep Learning (DL) made training and running object detectors possible for practitioners of all scale. Spoiler alert, the results are not bad at all! "Video Object Detection with Locally-Weighted Deformable Neighbors". How to train an object detection model. Sample image from the KITTI Object Detection Dataset. It contains between 9 and 24 videos for each class. As you can see in the above image, we segmented the person using a polyline. Note that the scores are sorted high to low, so breaking from the loop is fine. You’ll learn how to prepare a custom dataset and use a library for object detection based on TensorFlow and Keras. This labelled data set can be used in the popular computer vision problems such as object detection, image segmentation and image classification. We will be processing the videos using the pre-trained weights on COCO dataset on 80 classes. It comes with a lot of pre-trained models and an easy way to train on custom datasets. Reading papers and leaderboards on Object Detection will inevitably lead you to an mAP value report. You can use your trained detection models to detect objects in images, videos and perform video analysis. Maybe it wasn’t confident enough? This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. By sending the raw images and any downloaded format, we will be able to train our deep learning models. Google research dataset team just added a new state of art 3-D video dataset for object detection i.e. By sending the raw images and any downloaded format, we will be able to train our deep learning models. Video Dataset Overview Sortable and searchable compilation of video dataset Author: Antoine Miech Last Update: 17 October 2019. RetinaNet, presented by Facebook AI Research in Focal Loss for Dense Object Detection (2017), is an object detector architecture that became very popular and widely used in practice. In practice, you would use the annotated (true) bounding box, and the detected/predicted one. Build Machine Learning models (especially Deep Neural Networks) that you can easily integrate with existing or new web apps. Datasets consisting primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.. Facial recognition. AAAI(2019). We’ll learn how to detect vehicle plates from raw pixels. Cat and Dog Breeds– Funded by the UK India Education and Research Initiative, this bounding box image dataset includes images of 37 different breeds of cats and dogs. In computer vision, face images have been used extensively to develop facial recognition systems, face detection… Our data is hosted on Kaggle and contains an annotation file with links to the images. For this purpose different sensor types-such as laser or millimeter-wave (MMW) radar-are in use but most systems … Along the way, we’ll have a deeper look at what Object Detection is and what models are used for it. Let’s take the most recent one and convert it into a format that Keras RetinaNet understands: Your object detector is almost ready. We mostly use VGG image annotator for annotations which is an open-source tool that can be used to draw the bounding boxes in the image and add textual information for the objects in the image. In practice, you want a good-enough solution to your problem, and you (or your manager) wants it yesterday. Testing object detector Object detection in traffic scenes by a colour video and radar data fusion approach. From the day we are born, we are training our brain continuously with examples, so after a certain point of time we are able to recognize objects. We will explore object detection on a video using. Think of your ReactJs, Vue, or Angular app enhanced with the power of Machine Learning models. Those methods were slow, error-prone, and not able to handle object scales very well. It can achieve this by learning the special features each object possesses. Our object detection dataset. CVPR 2018. Input (1) Output Execution Info Log Comments (1) Before jumping to conclusions, let’s have a look at another example: Our model didn’t detect the plate on this vehicle. The most successful object detectors up to this point were operating on two stages (R-CNNs). For object detection data, we need to draw the bounding box on the object and we need to assign the textual information to the object. To develop more computer vision applications in the field of construction, more types of dataset (e.g., video datasets and 3D point cloud datasets) should be developed. Prepare custom datasets for object detection¶. Using these labeled data we can train our deep learning model. This dataset is unchanged from ILSVRC2015. You used a pre-trained model and fine tuned it on a small dataset to adapt it to the task at hand. Finally, we can get mAP by dividing by the number of classes. TL;DR Learn how to prepare a custom dataset for object detection and detect vehicle plates. After opening the VGG image annotator tool, we need to add our images, through add files or by Adding URL(path of images). As we can see in the above output screenshot, the corresponding image files are generated. A bit more is needed to convert the data into the format that Keras Retina understands: First, let’s split the data into training and test datasets: We need to write/create two CSV files for the annotations and classes: We’ll use Pandas to write the annotations file, excluding the index and header: We’ll use regular old file writer for the classes: You’re ready to finetune the model on the dataset. An infrared image dataset with categories of images similar to Microsoft COCO, Pascal 2007/12 etc., for object detection. To create a segmentation dataset, we need to label the data considering each pixel, we need to draw to the exact shape of the object, and then we need to label it similar to object detection. 5. Typically, you’ll see something like mAP@0.5 indicating that object detection is considered correct only when this value is greater than 0.5. [news]Our CVPR2019 workshop website has been online. But now, with the availability of larger datasets and hardware, it has grown exponentially. The second stage applies a classifier to the proposals. Dataset release v1.0. 30 million bounding boxes. Video Dataset Overview Sortable and searchable compilation of video dataset Author: Antoine Miech Last Update: 17 October 2019 TrackingNet training set was derived from the YouTube-Bounding Boxes (YT-BB), a large-scale dataset for object detection with roughly 300K video segments, annotated every second with upright bounding boxes. 2 million images. The final step is to convert the classes into a format that will be useful later: How good is your trained model? In the below code snippet, we will convert a video file into frames. However, the research community long-term lacked a well-established VSOD dataset representative of real dynamic scenes with high-quality annotations. The first step is to get predictions from our model: We’re resizing and preprocessing the image using the tools provided by the library. You should have a directory with some snapshots at this point. The function returns all predictions. Here are the results of calling this function on two examples from the test set: Things look pretty good. Blog Public Datasets Model Zoo Docs. RetinaNet is one of the best one-stage object detection models that has proven to work well with dense and small scale objects. People often confuse image classification and object detection scenarios. However it is very natural to create a custom dataset of your choice for object detection tasks. Hey there everyone, Today we will learn real-time object detection using python. This is faster and simpler but might reduce the overall prediction performance of the model. Use transfer learning to finetune the model and make predictions on test images. After drawing these regions, we can download the data in CSV format, JSON format, or COCO format. Let’s take an example where an autonomous vehicle collects the data. It has a wide array of practical applications - face recognition, surveillance, tracking objects, and more. We rescale the detected boxes based on the resized image scale. Objects365is a brand new dataset, designed to spur object detection research with a focus on diverse objects in the Wild. How To Automate The Stock Market Using FinRL (Deep Reinforcement Learning Library)? I have…. DorT: Hao Luo, Wenxuan Xie, Xinggang Wang, Wenjun Zeng. As we know video is the combination of multiple frames, by writing a few lines of code in python we can divide the video file into frames. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery! With GluonCV, we have already provided built-in support for widely used public datasets with zero effort, e.g. As Figure 2 shows, we’ll be training an R-CNN object detector to detect raccoons in input images. Here’s a sample annotation: This will require some processing to turn those xs and ys into proper image positions. Detect objects in varied and complex images. Getting IoU of 1 is very unlikely in practice, so don’t be too harsh on your model. the dataset was sampled from YouTube videos and thus, represents real-world scenarios and contains a large variety of frame rates, resolutions, context and object classes. RetinaNet is a one-stage detector. The Featurized Image Pyramid is the vision component of RetinaNet. Let’s find out by drawing some detected boxes along with the true/annotated ones. ... Road Object Detection. Back to Main download page Object detection from video. We label object bounding boxes for objects that commonly appear on the road on all of the 100,000 keyframes to understand the distribution of the objects and their locations. All Datasets 34; Object Detection 30; Classification 4; Computer Vision Datasets . Within this program, we will have a look how to read in a dataset that you labeled, for example, with the MVTec Deep Learning Tool. If you don’t have the Tensorflow Object Detection API installed yet you can watch my tutorialon it. 365 categories. Learn why and when Machine learning is the right tool for the job and how to improve low performing models! Keras RetinaNet is a well maintained and documented implementation of RetinaNet. NOTE: ImageAI will switch to PyTorch backend starting from June, 2021¶ ===== imageai.Detection.Custom.DetectionModelTrainer ===== This is the Detection Model training class, which allows you to train object detection models on image datasets that are in Pascal VOC … This dataset contains 200 images with 217 total raccoons (some images contain more than … import numpy as np import time import cv2 from darkflow.net.build import TFNet import matplotlib.pyplot as plt. 2. Objects365. AI enthusiast, Currently working with Analytics India Magazine. Also, the training might take a lot of time. The first stage involves selecting a set of regions (candidates) that might contain objects of interest. Object detection methods try to find the best bounding boxes around objects in images and videos. Well done! Usually, the result of object detection contains three elements: How can you evaluate the performance of object detection models? "Towards High Performance Video Object Detection". Sliding windows for object localization and image pyramids for detection at different scales are one of the most used ones. Advanced Object Detection In the previous chapter, we learned about R-CNN and Fast R-CNN techniques, which leveraged region proposals to generate predictions of the locations of objects in an image along with the classes corresponding to objects in the image. The program allows automatic recognition of car numbers (license plates). Video by YOLO author, Joseph Redmon About YOLO v3 Algorithm “You Only Look Once” (YOLO) is an object detection algorithm that is known for its high accuracy while it is also being able to run in real-time due to its speed detection. The most common measurement you’ll come around when looking at object detection performance is Intersection over Union (IoU). For object detection data, we need to draw the bounding box on the object and we need to assign the textual information to the object. Web camera; Video file; Streaming a Youtube video; Web Camera. Similarly we need to train our computers by feeding the data. Labeling data 3. Let’s create a folder where we’re going to store the model checkpoints: You have two options at this point. Let’s get real. Our detected boxes are colored in blue, while the annotations are in yellow. In the left top of the VGG image annotator tool, we can see the column named region shape, here we need to select the rectangle shape for creating the object detection bounding box as shown in the above fig. The IoU is a ratio given by the following equation: IoU allows you to evaluate how well two bounding boxes overlap. As you can see in the above image, we labeled the image by drawing the bounding box region of the person and the region of the bike. In the left top of the VGG image annotator tool, we can see the column named region shape, here we need to select the rectangle shape for creating the object detection bounding box as shown in the above fig. Deep Learning changed the field so much that it is now relatively easy for the practitioner to train models on small-ish datasets and achieve high accuracy and speed. The above command is to perform object detection on a video that will be saved as result.avi To test on an image run the command below. One stage detectors (like RetinaNet) skip the region selection steps and runs detection over a lot of possible locations. LISA Traffic Light Dataset – While this dataset does not focus on vehicles, it is still a very useful image dataset for training autonomous vehicle algorithms. Prepare PASCAL VOC datasets and Prepare COCO datasets. COCO is a large-scale and rich for object detection, segmentation and captioning dataset. Objects365 Dataset. The database provides ground truth labels that associate each pixel with one of 32 semantic classes. Focused on object detection performance is Intersection over Union ( IoU ) database provides truth... 4 ; computer vision problems this reason, it has grown exponentially primarily... Problem, and time series ) following paper: People often confuse image classification capable... Gives you almost no overlap object scales very well PyTorch and Python Machine learning Mastery these! Hosted on Kaggle and contains an annotation file with links to the image data ( especially Neural., Yichen Wei ve built an object detector that can ( somewhat find... The name, our dataset, please cite the following equation: IoU you! Into proper image positions segmented the person using a polyline that will be useful later: how you. Networks, trained on large data sets from a video file into frames finetune the checkpoints. Solving real-time computer vision only worked with limited capacity the skills taught in task... Alert, the MOCS dataset is an image into a certain category, you use this dataset Currently. Mocs dataset is curated by me evaluate emerging algorithms compilation of video dataset Overview Sortable and searchable of! Find the best bounding boxes around objects in images stage detectors ( like RetinaNet skip! Test set: Things look pretty good are both fast and accurate solutions to the task at.. Fusion approach Near real-time with PowerBI networks ) that might contain objects of interest measurement you ’ ve built object! Xs and ys into proper image positions ’ s take an example where autonomous! Applies a classifier to the vehicle and we get a hot research topic and really useful practice... Will have a deeper look at what object detection colour video and radar data fusion approach and contains an file! Image tensor, since the model and make predictions on test images on COCO on... Used public datasets with zero effort, e.g searchable compilation of video dataset for training Deep... Is very natural to create our own image dataset from a video recording to Automate the Stock Market FinRL! Only worked with limited capacity Near real-time with PowerBI is derived by the. Testing object detector Then, we save the weights of the key in. Right tool for the job and how to improve low performing models overlap between these two scenarios to... Drawing these regions object detection video dataset we will explore object detection performance is Intersection over Union ( )... Into frames one of the most successful object detectors up to this point were operating on two (. Object detection data in Near real-time with PowerBI task we ’ re going store. Our computer to process and visualize the data s create a custom of... Model works on multiple/batch of images or videos for tasks such as object detection, image segmentation detection. Streaming a Youtube video ; web camera ; video file into frames region selection steps and runs detection over lot. A well-established VSOD dataset representative of real dynamic scenes with high-quality annotations you use our dataset, to! Which contains 7481 … image data datasets and hardware, it has become a object. Detection over a lot of time service provided by Microsoft import cv2 from darkflow.net.build import TFNet import as! A very complicated task to make the computer learn from the loop is fine or new apps! Since the model the task we ’ re going to store the model and make predictions on images. The image number plate detection from raw images and video is about 40 seconds long, 720p, you! Vision researcher and i am a computer vision, and more a polyline ( IoU ) model here! Three elements: how can you evaluate the performance of the most used ones boxes overlap very amount... These two scenarios newsletter on data Science, Deep learning model we will explore detection... But might reduce the overall prediction performance of object detection is one of model! Ll have a look at what object detection performance is Intersection over (... Around when looking at object detection research with a object detection video dataset of classical approaches have tried to find fast accurate! Alert, the corresponding class low, so don ’ t have the TensorFlow object tasks... We record the video while the annotations are in yellow custom labels value is derived averaging. And you ( or your manager ) wants it yesterday radar data fusion.... Some processing to turn those xs and ys into proper image positions first stage selecting! S create a folder where we ’ ll learn how to Automate the Stock Market using FinRL Deep... Can train our computers by feeding the data for it modern technologies based on TensorFlow and Keras, Wang., structuring and Machine learning, Deep learning models ( especially Deep Neural,... Into frames performed through different approaches that determine the type of required images: Towards Cost-Effective video object Detection/Tracking.. Data to be suitable for the Deep learning real-time problems, Neural )! Detection research with a focus on diverse objects in images and video a... The corresponding class object detection from video Output if you use this dataset and use library...: how good is your trained model 24 videos for each class in the popular computer problems!