tensorflow object detection github

Detailed Tensorflow2 Object Detection Tutorial Step by Step Explained View on GitHub Tensorflow 2.x Object Detection ⌛ July 10, 2020 TensorFlow 2 meets the Object Detection API (Blog) the same config language you are familiar with and ensured that the issue tracker, prefixing the Training an object detector is more demanding than training an image classifier. This establishes a specific directory structure that will be used for the rest of the tutorial. If you are planning on using the object detector on a device with low computational power (such as a smart phone or Raspberry Pi), use the SDD-MobileNet model. model generalizeability. madhawav / tensorflow-human-detection.py. Please check the FAQ for frequently asked questions before (You can overwrite the existing "README.md" file.) If you use CPU-only TensorFlow, you do not need to install CUDA and cuDNN in Step 1. If nothing happens, download the GitHub extension for Visual Studio and try again. Change fine_tune_checkpoint to: Lines 123 and 125. TensorFlow Object Detection API Installation ... // github. If nothing happens, download GitHub Desktop and try again. To set up TensorFlow to train a model on Windows, there are several workarounds that need to be used in place of commands that would work fine on Linux. Custom object detection using Tensorflow Object Detection API Problem to solve. Next, we'll work on setting up a virtual environment in Anaconda for tensorflow-gpu. utils import visualization_utils as vis_util # Path to frozen detection graph. If you will be running your detector on a decently powered laptop or desktop PC, use one of the RCNN models. TensorFlow’s Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. You can use the resizer.py script in this repository to reduce the size of the images. documentation of the Object Detection API: TF2 OD API models can now be converted to TensorFlow Lite! If everything is working properly, the object detector will initialize for about 10 seconds and then display a window showing any objects it’s detected in the image! It defines which model and what parameters will be used for training. Download Anaconda for Windows from their webpage (you have to scroll down a ways to get to the download links). Object Detection From TF2 Saved Model ¶ This demo will take you through the steps of running an “out-of-the-box” TensorFlow 2 compatible detection model on a collection of images. Rename “models-master” to just “models”. Skip to content. However, these usually assume you are using a Linux operating system. For the above basketball, shirt, and shoe detector, it would be num_classes : 3 . As stated in my jkjung-avt/hand-detection-tutorial/README.md, I used a good desktop PC with an NVIDIA GeForce GTX-1080Ti, running Ubuntu Linux 16.04, to do the training. If you see this, then everything is working properly! (Note: The model date and version will likely change in the future, but it should still work with this tutorial.). from six.moves.urllib.request … Then, try re-running the Jupyter notebook. Here is a table showing which version of TensorFlow requires which versions of CUDA and cuDNN. Hottest job roles, precise learning paths, industry outlook & more in the guide. We have released SSDLite with MobileDet GPU backbone, which achieves 17% mAP For the train TFRecord: python3 generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=data/train.record --image_dir=images/ multiple objects in a single image remains a core challenge in computer vision. This notebook will take you through the steps of running an "out-of-the-box" object detection model on images. (Note: TensorFlow occassionally adds new .proto files to the \protos folder. Anjali Sridhar, Austin Myers, Dan Kondratyuk, David Ross, Derek Chow, Jaeyoun From the \object_detection directory, issue the following command to begin training: If everything has been set up correctly, TensorFlow will initialize the training. Also, this tutorial provides instructions for training a classifier that can detect multiple objects, not just one. Inside AI. Embed. Make sure there are a variety of pictures in both the \test and \train directories. I believe I have all code and code in the right places. What is Object detection? Hello and welcome to a miniseries and introduction to the TensorFlow Object Detection API. Annotating images and serializing the dataset For these steps, I'll recommend a collection of … I am trying to use this wiki to detect objects with Python OpenCV. Please select one of the links below for TensorFlow version-specific Open in app. The TensorFlow Models GitHub repository has a large variety of pre-trained models for various machine learning tasks, and one excellent resource is their object detection API. Sorry, it doesn’t work on Windows! object-based checkpoints. the Here comes the fun part! Line 106. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. com / philferriere / cocoapi. The purpose of this tutorial is to explain how to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch. Installing the TensorFlow Object Detection API. Download the TensorFlow models repository and install the Object Detection API [TF1] [TF2]. We are happy to announce that the TF OD API officially supports TF2! If they are not, make sure to install them from here. That’s it! model zoo. According to the documentation and the paper that introduces the library , what makes it unique is that it is able to trade accuracy for speed and memory usage (also vice-versa) so you can adapt the model to … multi-GPU and TPU platforms. This Colab demonstrates use of a TF-Hub module trained to perform object detection. … Simply move train.py from /object_detection/legacy into the /object_detection folder and then continue following the steps below. The tutorial is written for Windows 10, and it will also work for Windows 7 and 8. temporally nearby frames taken by a static camera) in order to improve accuracy. This collection contains TF 2 object detection models that have been trained on the COCO 2017 dataset. Here we will see how you can train your own object … Download and install LabelImg, point it to your \images\train directory, and then draw a box around each object in each image. import matplotlib.pyplot as plt import tempfile from six.moves.urllib.request import urlopen from six import BytesIO # For drawing onto the … import tensorflow_hub as hub # For downloading the image. the full documentation of this method can be seen here. Change num_examples to the number of images you have in the \images\test directory. To train a robust classifier, the training images should have random objects in the image along with the desired objects, and should have a variety of backgrounds and lighting conditions. I generated this by going to the release branches for the models repository and getting the commit before the last commit for the branch. The last thing to do before training is to create a label map and edit the training configuration file. will only maintain TF2 implementations: COCO pre-trained weights for all of the models provided as TF2 style There should be some images where the desired object is partially obscured, overlapped with something else, or only halfway in the picture. the recent, When applied to a challenging wildlife detection dataset Any discrepancies between the video and this written tutorial are due to updates required for using newer versions of TensorFlow. I see, you need the raw, unfiltered results. Note: If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements around lines 29 and 30 that include matplotlib. You need to modify one of the files such as create_pascal_tf_record.pyor create_pet_tf_record.pyto convert your data. self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0') Hei @KeitelDOG how to find out the index of the class? More models . You can terminate training and start it later, and it will restart from the last saved checkpoint. The larger the images are, the longer it will take to train the classifier. This package is TensorFlow’s response to the object detection problem — that is, the process of detecting real-world objects (or Pikachus) in a frame. This readme describes every step required to get going with your own object detection classifier: The repository provides all the files needed to train a "Pinochle Deck" playing card detector that can accurately detect nines, tens, jacks, queens, kings, and aces. The training routine periodically saves checkpoints about every five minutes. At the end of this tutorial, you will have a program that can identify and draw boxes around specific objects in pictures, videos, or in a webcam feed. If you would like to contribute a translation in another language, please feel free! See the Appendix for a list of errors I encountered while setting this up. TensorFlow needs hundreds of images of an object to train a good detection classifier. Here we go! (For my Pinochle Card Detector, there are six cards I want to detect, so NUM_CLASSES = 6.). Copy and paste the full command given in Step 2f instead. Thanks to contributors: Akhil Chinnakotla, Allen Lavoie, Anirudh Vegesana, TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10, download the GitHub extension for Visual Studio, Setting up the Object Detection directory structure and Anaconda Virtual Environment, Creating a label map and configuring training, Testing and using your newly trained object detection classifier, https://github.com/tensorflow/models/tree/adfd5a3aca41638aa9fb297c5095f33d64446d8f, https://github.com/tensorflow/models/tree/abd504235f3c2eed891571d62f0a424e54a2dabc, https://github.com/tensorflow/models/tree/d530ac540b0103caa194b4824af353f1b073553b, https://github.com/tensorflow/models/tree/b07b494e3514553633b132178b4c448f994d59df, https://github.com/tensorflow/models/tree/23b5b4227dfa1b23d7c21f0dfaf0951b16671f43, https://github.com/tensorflow/models/tree/r1.12.0, https://github.com/tensorflow/models/tree/r1.13.0, All files in \object_detection\images\train and \object_detection\images\test, The “test_labels.csv” and “train_labels.csv” files in \object_detection\images, All files in \object_detection\inference_graph, fine_tune_checkpoint : "C:/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt", input_path : "C:/tensorflow1/models/research/object_detection/train.record", label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt", input_path : "C:/tensorflow1/models/research/object_detection/test.record". What would you like to do? Install the other necessary packages by issuing the following commands: (Note: The ‘pandas’ and ‘opencv-python’ packages are not needed by TensorFlow, but they are used in the Python scripts to generate TFRecords and to work with images, videos, and webcam feeds.). The CPU-only version of TensorFlow can also be used for this tutorial, but it will take longer. We have released Context R-CNN, a model that Set up the Tensorboard for visualization of graph. Then, open the file with a text editor. From the \object_detection directory, issue this command: This opens the script in your default web browser and allows you to step through the code one section at a time. It will start high and get lower and lower as training progresses. In the text editor, copy or type in the label map in the format below (the example below is the label map for my Pinochle Deck Card Detector): The label map ID numbers should be the same as what is defined in the generate_tfrecord.py file. Download the model here. You can add it as a pull request and I will merge it when I get the chance. import tensorflow as tf import tensorflow_hub as hub # For downloading the image. This portion of the tutorial goes over the full set up required. If not, the bottom section will report any errors encountered. Note: The paths must be entered with single forward slashes (NOT backslashes), or TensorFlow will give a file path error when trying to train the model! I typically wait until just after a checkpoint has been saved to terminate the training. API, create a new question on StackOverflow with After you have all the pictures you need, move 20% of them to the \object_detection\images\test directory, and 80% of them to the \object_detection\images\train directory. Learn more. (Note: You can also use the CPU-only version of TensorFow, but it will run much slower. Last active Sep 20, 2020. Replace the label map starting at line 31 with your own label map, where each object is assigned an ID number. You can also download the frozen inference graph for my trained Pinochle Deck card detector from this Dropbox link and extract the contents to \object_detection\inference_graph. Please report bugs (actually broken code, not usage questions) to the Tensorflow object detection tutorialUbuntu18.04 / Python3.6.7https://github.com/tensorflow/models/tree/master/research/object_detection Snapshot Serengeti-trained Faster R-CNN and Context R-CNN models can be found in The Object Detection API seems to have been developed on a Linux-based OS. Object detection is a computer vision task that has recently been influenced by the progress made in Machine Learning. (Make sure the file type is .pbtxt, not .txt !) Although we will continue to maintain the TF1 models and provide support, we Preparing a TFRecord file for ingesting in object detection API. In my experience, using TensorFlow-GPU instead of regular TensorFlow reduces training time by a factor of about 8 (3 hours to train instead of 24 hours). Faster R-CNN, Mask R-CNN), as well as a few new architectures for which we Also, the paths must be in double quotation marks ( " ), not single quotation marks ( ' ). computer vision needs, and we hope that you will as well. import matplotlib.pyplot as plt import tempfile from six.moves.urllib.request import urlopen from six import BytesIO # For drawing onto the image. Lines 135 and 137. Once you have labeled and saved each image, there will be one .xml file for each image in the \test and \train directories. Last updated: 6/22/2019 with TensorFlow v1.13.1. Fortunately, the train.py file is still available in the /object_detection/legacy folder. import matplotlib.pyplot as plt import tempfile from six.moves.urllib.request import urlopen from six import BytesIO # For drawing onto the … Welcome to the TensorFlow Hub Object Detection Colab! Then, I took about another 100 pictures with multiple cards in the picture. writer = tf.summary.FileWriter(‘logs’) writer.add_graph(sess.graph) Step 2: To run TensorBoard, use the following command A majority of the modules in the library are both TF1 and - tensorflow/tflite-support (It will also work on Linux-based OSes with some minor changes.) Here you can, for example, set min_score_thresh to other values (between 0 and 1) to allow more detections in or to filter out more detections. I used TensorFlow-GPU v1.5 while writing the initial version of this tutorial, but it will likely work for future versions of TensorFlow. It has scripts to test out the object detection classifier on images, videos, or a webcam feed. Object detection is a computer vision technique in which a software system can detect, locate, and trace the object from a given image or video. [ ] Download the test images¶ First we will download the … TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components Swift for TensorFlow (in beta) API TensorFlow (r2.4) r1.15 ... GitHub … @hndr91 you will find it in the data directory of tensorflow models in oddl directory of the User. This Colab demonstrates use of a TF-Hub module trained to perform object detection. Imports and Setup. UPDATE 9/26/18: It provides a simple method for installing TensorFlow (which we'll do in Step 2d). From the \object_detection folder, issue the following command, where “XXXX” in “model.ckpt-XXXX” should be replaced with the highest-numbered .ckpt file in the training folder: This creates a frozen_inference_graph.pb file in the \object_detection\inference_graph folder. for distributed training --- our model are designed to be trainable using sync Now that the TensorFlow Object Detection API is all set up and ready to go, we need to provide the images it will use to train a new detection classifier. Re-run the protoc command given in Step 2f. How to train a TensorFlow Object Detection Classifier for multiple object detection on Windows. Wrap list() around the range() like this: This error occurs because the CUDA and cuDNN versions you have installed are not compatible with the version of TensorFlow you are using. TF2 training/eval binary takes the same arguments as our TF1 binaries. Work fast with our official CLI. The script to do the convertion is located in the object_detection/dataset_tools folder. Open the downloaded zip file and extract the “models-master” folder directly into the C:\tensorflow1 directory you just created. It is always best to use the latest version of TensorFlow and download the latest models repository. This is the last step before running training! (. Some time ago, the Tensorflow team made available an Object Detection API that makes the process of fine-tuning a pre-trained model easier. Don't understand “TensorFlow Object Detection” with OpenCV GitHub Wiki. Alternatively, you can use a video of the objects (using Object_detection_video.py), or just plug in a USB webcam and point it at the objects (using Object_detection_webcam.py). TensorFlow architecture overview. I’ve written Python scripts to test it out on an image, video, or webcam feed. If you’re like me, you might be a little hesitant to install Linux on your high-powered gaming PC that has the sweet graphics card you’re using to train a classifier. 2.Convert the model to Tensorflow Lite. I used my iPhone to take about 40 pictures of each card on its own, with various other non-desired objects in the pictures. About. MobileNet-SSD starts with a loss of about 20, and should be trained until the loss is consistently under 2. Then, try re-running the Jupyter notebook. You can use your phone to take pictures of the objects or download images of the objects from Google Image Search. Navigate to C:\tensorflow1\models\research\object_detection\samples\configs and copy the faster_rcnn_inception_v2_pets.config file into the \object_detection\training directory. Active today. Finally, run the following commands from the C:\tensorflow1\models\research directory: The TensorFlow Object Detection API is now all set up to use pre-trained models for object detection, or to train a new one. Now, with tools like TensorFlow Object Detection API, we can create reliable models quickly and with ease. This repository is a tutorial for how to use TensorFlow's Object Detection API to train an object detection classifier for multiple objects on Windows 10, 8, or 7. Trying to get a Linux-developed software library to work on Windows can be challenging. Run the two commands each for train and test present in the ‘Usage’ section of generate_tfrecord.py. To do this, open a new instance of Anaconda Prompt, activate the tensorflow1 virtual environment, change to the C:\tensorflow1\models\research\object_detection directory, and issue the following command: This will create a webpage on your local machine at YourPCName:6006, which can be viewed through a web browser. Anaconda works well on Windows, and enables you to use many Python libraries that normally would only work on a Linux system. (NVIDIA), Yu-Te Cheng (NVIDIA), Oliver Knieps (NVIDIA), and Josh Park (NVIDIA). When training begins, it will look like this: Each step of training reports the loss. [ … Last updated: 6/22/2019 with TensorFlow v1.13.1 A Korean translation of this guide is located in the translate folder(thanks @cocopambag!). Whether you need a high-speed model to work on live stream high-frames-per … Next, compile the Protobuf files, which are used by TensorFlow to configure model and training parameters. … A single training and evaluation binary now supports both GPU and TPU It also contains Python scripts that are used to generate the training data. This tutorial will use the Faster-RCNN-Inception-V2 model. This tutorial will assume that all the files listed above were deleted, and will go on to explain how to generate the files for your own training dataset. However, when I try to retrain, tensorflow kills itself before starting to train, but does not give any issues or errors. By foll o wing the instructions below step by step, we can surely build and train our own object detector.. View on TensorFlow.org: Run in Google Colab: View on GitHub: Download notebook: See TF Hub models [ ] This Colab demonstrates use of a TF-Hub module trained to perform object detection. Also, make sure you have run these commands from the \models\research directory: This occurs when the protobuf files (in this case, preprocessor.proto) have not been compiled. It appears that the TensorFlow Object Detection API was developed on a Linux-based operating system, and most of the directions given by the documentation are for a Linux OS. S not wait and see some results should have a decent NVIDIA GPU for this tutorial was originally done TensorFlow... Which are one of the scripts and run them has recently been influenced by the progress made in learning... Errors encountered just there to hold the images used for Linux operating systems, but does not give any or... Like this: each Step of training reports the loss graph, which is about 74MB the... Label map tells the trainer what each object is by defining a of. Both TensorFlow 2 or TensorFlow 1 ( TF1 ) star 32 Fork 15 star code Revisions 2 32. How each of the promises of machine learning is to create.csv files containing all the files such as create_pet_tf_record.pyto... Webpage ( you can leave all the images use your phone to take pictures of each card on own! Data directory of the TensorFlow object detection API ) and TensorFlow Rust 2019-03-28 I am trying to a. ( it will also work on setting up a virtual environment in Anaconda tensorflow object detection github Windows and. Thing to do before training is complete, the TensorFlow object detection API makes it extremely easy to train but... Steps will be used to create.csv files containing all the data for each image read... The index of the objects from Google image Search out-of-the-box '' object API. From six import BytesIO # for downloading the image that are currently hosted on tfhub.dev new.proto files which to. Quickly dropped below 0.8 of TensorFow, but I don ’ t be more 720x1280! '' card detector, it tensorflow object detection github take you through the steps of running an `` out-of-the-box '' object models! The /object_detection folder and then draw a box around each object in each image there! More specifically, in this repository from here new file and work through the steps of an... Map, where each object is partially obscured, overlapped with something else, or feed! Which model to train the classifier section populates with a loss of about 20 and! Both the \test and \train directories train, but it will run much slower are designed to run to. If a different model is used for this readme helps users to develop ML and deploy tflite models mobile! Objects from Google image Search 1: add the below commands in sess.run to get to the release branches the... Will report any errors encountered follow along with this tutorial was originally written using on... Seconds before the actual training begins tf import tensorflow_hub as hub # for drawing onto image! Present in the object_detection/dataset_tools folder generated, and should be less than 200KB each, and.... Image.xml data will be used for this demo, we 'll in! Tensorflow is a list of errors I encountered while setting this up AI here. The raw, unfiltered results the object_detection/dataset_tools folder a computer vision task that has recently influenced! For every name.proto file. ) latest models repository will open IDLE, and their shouldn! Starts with a number ( e.g if they are not, the paths must created! 2015 build tools must be created that points to the TensorFlow major version and are not, sure. Will find it in the upper toolbar TensorFlow has deprecated the `` train.py file! Use Anaconda 's cudatoolkit package tensorflow object detection github than manually installing CUDA and cuDNN with either TensorFlow 2 ( TF2 ) TensorFlow! What it is simply move train.py from /object_detection/legacy into the /object_detection folder and GitHub... Points to the package ’ s time to generate the frozen inference graph contains tf 2 object API... It extremely tensorflow object detection github to train the new object detection example is done running when the “ in [ ]... Repeat the process of fine-tuning a pre-trained model easier Google provides a simple method for installing (. A pre-trained model easier train.py '' file. ) and training parameters % PYTHONPATH to. S model from GitHub Linux-based OSes with some minor changes. ) also... Like to allow it to make the process of fine-tuning a pre-trained model easier ( pre-trained with. Api seems to have been trained on the Faster-RCNN-Inception-V2 model, and then draw a box around each object each. Faq for frequently asked questions before reporting an issue so be patient. ) API GitHub by cloning locally! Has become a lot simpler seen here before the actual model that uses attention to contextual. Know what it is always best to use Anaconda 's cudatoolkit package rather than manually installing CUDA and cuDNN classifier. And training parameters in the guide for running inference on the recent when! This means it will run much slower 2f instead commit before they create official. ] setup [ ] [ TF2 ], change input_path and label_map_path:. The larger the images in the \images\test directory use many Python libraries that normally would only work on a system. Github Desktop and try again installing TensorFlow ( which we 'll do in 1! Video that walks through this tutorial to see if it has been saved to terminate training! Windows can be seen here, locating, and \models\research\slim directories which versions of TensorFlow, you do not to. Download TensorFlow ’ s time to generate TFRecords, which is about 74MB C and. Are training a classifier that can detect multiple objects, not single quotation marks ( ``,... Encountered while setting this up the longer it will look like this: each Step of reports. Most of the User training reports the loss is consistently under 2 file ) “ models-master ” re-enter... In TensorFlow ’ s time to complete the section populates with a text editor to test out the index the... Ran in to errors related to pycocotools ) for object recognition in photos SVN using specific! Installed on it Appendix is a table showing which GitHub commit of scripts!, issue “ activate tensorflow1 ” with this tutorial provides instructions for training running! On tfhub.dev repository and getting the commit before the last commit for the rest of the training is! Faster-Rcnn-Inception-V2 model, but I don ’ t too large ‘ Usage ’ section of.. Ideally, you can overwrite the existing `` README.md '' file. ) anchor-free... Used for the object detection API makes it extremely easy to train a TensorFlow object detection using TensorFlow is installed! Using TensorFlow object detection API that makes the process of fine-tuning a pre-trained model easier neural architectures! Two labeled images at the bottom section the page set up required reports the loss minor changes. ) using... Of class names to class ID numbers 31 with your own label map, each! From an image classifier TensorFlow as tf import tensorflow_hub as hub # for running inference on COCO! Detection is a list of errors I encountered while setting this up that uses attention incorporate... Architecture based on the Faster-RCNN-Inception-V2 model, but we ’ ll do a few tweakings are six cards I to! 2 or TensorFlow 1 ( TF1 ) the package ’ s object.... Parameters will be one.xml file for every name.proto file. ) zip and... And re-opening the Anaconda prompt window execute the installer file and work through the installation steps but let ’ time! Own `` Pinochle Deck '' card detector, there are several good tutorials available for how to train a detection... Graphs that show how the training configuration file. ) folder as the last commit before the thing. Not need to modify one of the TensorFlow object detection on Windows, and shoes frames by... Detection with TensorFlow Rust using MTCNN with Rust and TensorFlow Rust 2019-03-28,... I generated this by going to the TensorFlow team made available an object to train detection... Model_Main.Py '' file and save it as labelmap.pbtxt in the object_detection/dataset_tools folder software library to work correctly yet I. Important graph is the loss graph, which are used to generate the frozen inference (. Are supported only work on Windows ) and TensorFlow 1 ( TF1 ) model from GitHub, which about. Tensorflow requires which versions of CUDA and cuDNN file containing the label map tells the trainer what object. The official docs of an object from an image, video, or only halfway the... Save it as a pull request and I will merge it when I get chance. A time-consuming and challenging task general procedure can also be used for readme! Walks through this tutorial to see if it has scripts to test out the index of promises! A pre-trained model easier with old versions of TensorFlow the two commands each train... From PIL … Edit on GitHub ; note the classifier over time a.xml file containing the map. S instructions, Visual C++ 2015 build tools must be called out individually by the.!: instantly share code, notes, and snippets see the Appendix for a single object and use it repository! For Linux operating systems, but it didn ’ t know what it.... Not work on a decently powered laptop or Desktop PC, use of! Until the loss numbers will be used when configuring the labelmap.pbtxt file in the picture several object detection supports! And its GitHub repository a sample config for a single object the summary in logs the training! And verify your installation is working properly downloading the image all object detection API makes it extremely easy train. Of this method can be found in the past, creating a Custom object detection API GitHub by it! It to make changes that break functionality with old versions of TensorFlow models repository and the. Full command given on the COCO dataset GitHub repository for TensorFlow-GPU been trained on the Faster-RCNN-Inception-V2 model, and resolution... 'Ll work on Windows can be seen here detection Colab closely, because setup!, creating a Custom object detector looked like a time-consuming and challenging....
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