Course Content Introduction and Course Overview –> 2 lectures • 13min. I am also thinking about the same approach as you described and will try it as long as I have time. I do know, the amount data required is proportional to the architecture parameter count. Setup Imports and function definitions # For running inference on the TF-Hub module. I also try to use object detection for OCR but I have 14 classes and can only detect 9 of them with model_main. It may also catch your attention that we are doing this from VASmalltalk rather than Python. Object Detection in Images. As the name suggests, it helps us in detecting, locating, and tracing an object from an image or camera. that right ? Object Detection plays a awfully vital role in Security. I assume that the release Tensorflow SSD mobilenet is under SSD300 architecture, not SSD500 architecture : And this is why I was trying to change the image_resizer into larger value (512 x 512); however, it still not worked. The task of object detection is to identify "what" objects are inside of an image and "where" they are. Yes, even rendering bounding boxes, labels and scores. I have no clue on how to approach the problem with the watches though. @eumicro how did you edit the config file to obtain that good detection? I'll give it a try asap and keep everyone updated on how it works out. I'm talking about SSD-FPN with resnet50 or mobilenet. Chunfang Deng, Mengmeng Wang, Liang Liu, and Yong Liu arXiv 2020; MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection @jungchan1 sorry I could not provide my trained work. On Fri, Jun 15, 2018, 11:59 hengshan ***@***. Y = X * 960/1280, Feel free to adjust it to your needs. I found extremely useful to modify the ssd_anchor_generator min_scale and max_scale based on the dimensions of the objects (0.1 and 0.5). On the other hand, if you aim to identify the location of objects in an image, and e.g. Thanks a lot for the resources. is the loss in your graph for the traffic light detection in percent? My problem is the same, because I get values between 1 and 2. However, when I stop around 12k and feed with the test dataset (around 90 images for a short try). how?). Quite a same issue i am facing with ssd_mobilenet_v2_coco_2018_03_29 pre-trained model. I'm interested in a good accuracy with a great speed, so I need SSD architecture. This project based Faster rcnn + FPN, which is accurate to detect small objects. I was trying to avoid this since the manual crop and re-annotate will take few days I assume :p. In my case, I also used the pre-trained SSD mobilenet on coco dataset and fine tuning with the traffic light dataset. Object Detection Introduction of Object Detection What you’ll learn Object Detection. In the previous example (with LabelImage) we processed the “raw” results just as TensorFlow would answer it. @preronamajumder Did you use transfer learning or you train the model from scratch? For example, a model might be trained with images that contain various pieces of fruit, along with a label that specifies the class of fruit they represent (e.g. I think the trend of the total loss is okay. The only thing you must do is to uncompress the .tar.gz and simply change the one line where you specify the graph (graphFile:) to use rcnn_inception_resnet_v2 and you will see the results are much better: You can see with mobilenet_v1 the spoon was detected as person, the apple on the left and the bowl were not detected and the cake was interpreted as a sandwich. Object Detection with TensorFlow and Smalltalk. Training Custom Object Detector¶. My original images are 512x512 I am thinking about cropping them to 300x300 around the areas of interest and create the TFrecords file from the cropped ones. For example, the difference between the 200 S (in the pic) and 200 C would be.. the S and C in the badging on the car. Changing the learning may help, because the one exists now in the pipeline.config is probably not what you need and it the one that was used for the training that was done from scratch. Localisation loss is fluctuating and loss is quite high even after 50K steps. An interesting task for me is to fine-tuning the SSD_mobilenet_v1_coco_2017_11_17 with Bosch small traffic light dataset. Our work was also inspired by this and this Juypiter notebooks for the demo. Apart from those questions above, a couple of questions which I always confuse myself with: @Deep-Sek If you want to train an SSD512 model, you need to start from scratch. Cars -> Attached below is a Chrysler car rear view. you just put size=(2,2) 1000 / 2 = 500. have you tried the stock SSD_mobilenet_v1_coco_2017_11_17 without training and see the result visually? Smalltalk expert working as a Senior Software Engineer at Instantiations. In the previous post you can see that all the demo was developed in the class LabelImage. Detected Objects Publishing on Web. It works great. To run the examples, you must first check the previous post to see how to install VASmalltalk and TensorFlow. @Ekko1992 I skipped OCR techniques all together because I thought since this is "OCR in the wild" where we don't control the environment, the performance would not be good. I used Tensorflow's Object Detection API for the training. Trying to train model with 7 classes (Pedestrian;Truck;Car;Van;Bus;MotorBike;Bicycle). I am wondering if the following approach would work with SSD mobilenet V1/V2 models: I will create a dataset consisting of individual numbers, logos and the whole billboard. I found some time to do it. This example runs the basic mobilenet_v1 net which is fast but not very accurate: In the tensorflow-vast repository we only provide a few frozen pre-trained graphs because they are really big. Object Detection using Tensorflow is a computer vision technique. I have 10 classes that I'm working with. My images are 600x600 size but with resizing in the config file 300x300. The dimensions of the objects range from 80px to 400px. Now later i got some new data of 10 more classes like Paperboat, Thums up etc and I want my model to trained on these too. so if you have a image that is 1000x1000 and you need 500x500 tiles. img.shape = (260,346,3). We’ll occasionally send you account related emails. Recognizing objects in images with TensorFlow and Smalltalk, Getting Started with Nvidia Jetson Nano, TensorFlow and Smalltalk. For example, first annotate the car to localize it from the environment. Users are not required to train models from scratch. left is 300x300, right is 260x346 I have a question regarding the configuration of SSD. I consider my objects medium size but SSD mobilenet v1 gives low accuracy and the training time is long. Both has gave me same orientation: OK i will try 224224 Now that we have done all the above, we can start doing some cool stuff. In this post I just took 2 of them (mobilenet_v1 and rcnn_inception_resnet_v2) but you can try with anyone. 谢谢回复。 Detecting Objects and finding out their names from images is a very challenging and interesting field of Computer Vision. If you want smooth UI you can track feature points with This Colab demonstrates use of a TF-Hub module trained to perform object detection. As shown in a previous post, naming and locating a single object in an image is a task that may be approached in a straightforward way. Train.py loss does something weird doing great for the first epoch and then goes expotentially to billioons. What is Object detection? I'm finding several problems in obtaining a good detection on small objects. I am also facing a problem of recognizing small objects on the image. @Tsuihao i had a similar problem and i needed to slice the image into smaller tiles/crops. @Tsuihao Any progress on this method ? Tensorflow is crap and below-par piece of shitty library written for the benefit of Google cloud. I am not sure how the performance will be of cropping training images. The Object Detection API provides pre-trained object detection models for users running inference jobs. https://arxiv.org/abs/1708.05237 They modified SSD OHEM and IOU criterion to be more sensitive to small object like faces. If so how did you get around it? Finally, thanks to Gera Richarte for the help on this work and to Maxi Tabacman for reviewing the post. Object Detection Tutorial Getting Prerequisites While starting to implement this new demo we detected a lot of common behaviors when running pre-trained frozen prediction models. want to train a model to detect my hand, yes only one class and run the Local implementation If you want to classify an image into a certain category, it could happen tha… Would this help in any way? comment the following in your pipeline.config file. #random_horizontal_flip { when you crop it into 300 x 300, the annotated image coordinate system need to be updated. Here is the code, its far from perfect but i needed a quick solution. The pre-trained model can only be fine-tuned as SSD300 model. S3FD: Single Shot Scale-invariant Face Detector For detecting the object, we have used different deep learning algorithms as object classifiers namely convolution neural network and logistic regression. Then we will detect the whole billboard at first. Maybe the small traffic lights are too small for SSD? For this I modify the preprocessor as in the pull request #8043 and used the configuration, On Stack Overflow someone explained how to test the augmentation. Thank you. Thanks @gerasdf & @instantiations #TensorFlow #MachineLearning #DeepLearning #AI #VASmalltalk @machinelearnflx pic.twitter.com/LV8XnodkNe. Will this work correctly as well? DHL - 1248265 So, up to now you should have done the following: Installed TensorFlow (See TensorFlow Installation). however i already labelled my dataset and i was not sure what size of tiles were suitable for training. i.e - In this post, I will explain all the necessary steps to train your own detector. [ ] An idea I had, was to first train mobilenet base network, fine tuning from the checkpoint trained on the coco dataset or a classification checkpoint, to just classify small crops of the the objects of interest. Then it shouldn't matter. Hi, i have a problem related with this, but it's a little different. Another improvement was to modify the file ssd_mobilenet_v2_feature_extractor.py to use layer_15/expansion_output as first feature map and the rest are all new layers (no more layer_19). This is a 200 S. I have a dataset of the rear view of the car. Is that from the Tensorboard? In general, if you want to classify an image into a certain category, you use image classification. 我试图避免这种情况的所有图像，因为手动裁剪和重新注释需要几天我假设：p。, 就我而言，我还在coco数据集上使用了预先训练过的SSD mobilenet，并使用交通灯数据集进行了微调。. This converged to a loss of 1.8 after 86000 steps. And what framework did you use for training, caffe or tensorflow? In this post, we explain the steps involved in … Did you manually re-annotate them or there is some crop image tool can help you do this? Try setting a scheduled decay of LR. Did your loss function seemed to converge ? Maybe the last way is really like what you say, crop and re-annotate everything. @dexception Which version of tensorflow you're reffering to as the old version? However, with ObjectDetectionZoo the results were a bit more complex and in addition we needed to improve the readability of the information, for example, to render the “bounding boxes”. Huge progress: #Tensorflow Object Detection with #Smalltalk! Completely forgot about the annotation. I just had an idea reading this discussion here where I can do weird annotations. I can see that the network having trouble with detections if you used a different aspect ratio to capture raw data (before resizing) and then resized that to 1:1. Also, will take a look at the paper and try that too. After we finished the refactor it was quite easy to add a new subclass ObjectDetectionZoo. Also, Faster-RCNN. from which file you removed first two layers ? Can you tell me what you think of that paper? In the following video I’ll show you how you can easily use a pre-trained model to detect objects in your webcam video. Y = 259.81._. [ ] Setup [ ] [ ] #@title Imports and function definitions # For running inference on the TF-Hub module. In my case I have program that generates all of my training data, so I can easily change the training data image size (which will then change the annotations). I want to resize the image to smaller size like 100*100, the speed is much fast, but the presicion is very bad. By now, (thanks to experiments by @AliceDinh ) we know that FPN as a feature extractor matched with SSD helps increase accuracy on small objects. I will suggest you to: Hey, I read that you struggled with resizing/cropping and then labeling again. Hey guys, A quick hijack of the post here. You could try training it on smaller images and feed in overlapping crops of size 300x300 that tile the original image, which could be bigger. However, this result can be foreseen due to the fact that SSD_mobilenet_v1_coco_2017_11_17 trained with the COCO dataset. 1. We keep pushing to show TensorFlow examples from Smalltalk. (With FasterRCNN, after 2K steps I get loss ~=0.02). an apple, a banana, or a strawberry), and data specifying where each object appears in the image. Then go back to SSD and fine-tune the model from these weights trained to classify. Already on GitHub? 90e3=X * X * 960/1280 = X^2 * 960/1280, This post will walk you step by step through the process of using a pre-trained model to detect objects in an image. I described how I fine tuned and trained the SSD MobileNet here (only in German, sorry): http://eugen-lange.de/german-traffic-sign-detection/. Let's say: Can I randomly pull data from other datasets and call it background class? I trained with vanilla Mobilenet-SSD and it didn't seem to help. The TensorFlow Object Detection API’s validation job is treated as an independent process that should be launched in parallel with the training job. If you would like better classification accuracy you can use ‘mobilenet_v2’, in this case the size of the model increases to 75 MB which is not suitable for web-browser experience. The real size of a billboard is pretty big, but we need to detect numbers from a distance, so the numbers would actually become small, although you could still easily recognize them on the phone screen. This is the adapted script to visualize the effect of the above operation. I collected the watch dataset with the image size at 2592x1944 (4:3) and I RESIZE it to 640x480 (4:3) as input image to the neural network. For those who are visiting... let me break down the entire story for you. There are bugs depending upon which version of tensorflow your using that is why if your working on new version this problem should not come in your way. -- i'm not sure how you've plotted this image - but I recommend to open tensorboard (in case you didn't) - the events are written there periodically an you will get also some images from your validation set with their detections. 300 * 300 = 90e3, In particular, I created an object detector that is able to recognize Racoons with relatively good results.Nothing special they are one of m… Custom object detection.In the next blog I will write about how to use this model along with OpenCV to build an object detection solution to generate outputs like the above image. However, you can very easily download additional ones and use them. 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. Everything in github: https://t.co/4ujjn3vxw2. As can be seen attached image. i will probably make a library some day. But preserving aspect ratio doesn't really do anything. Maybe is better to move to SSD inception v2? Suppose i train tensorflow faster Rcnn_inception on any custom data having 10 classes like ball, bottle, Coca etc.. and its performing quite well. They are also useful for initializing your models when training on novel datasets. Y = 259.81._, Rounding X and Y to integers to keep X * Y<90e3 with minimal wasted bytes finds the optimal new size to be 346x260 with 40 * 3 wasted bytes. — Yes, I had successfully trained faster rcnn and obtained an accurate result. 100x100 is too small for do you really need these 6 output branches? Do you guys think this will help? Check out the previous post to see why I believe Smalltalk could be a great choice for doing Machine Learning. #data_augmentation_options { Finally, you can play with custom object detection by TensorFlow. Given an input image, the algorithm outputs a list of objects, each associated with a class label and location (usually in the form of bounding box coordinates). Finally, you can also try with different pictures. I have a problem with ssd_mobilenet_v2_coco. boxes: {label: Green, occluded: false, x_max: 752.25, x_min: 749.0, y_max: 355.125, y_min: 345.125}. Hey there everyone, Today we will learn real-time object detection using python. count the number of instances of an object, you can use object detection… @Tsuihao you cropping already annotated images. X = sqrt(90e3 * 1280/960) = 346.41, so for 300x300, the ratios would be calculated for 300. but for your case 260x346, if you input either 260 or 346, the resulting bounding boxes generated by the tensorrt model in the edge device will be different than the ones generated by the tensorflow model in your pc. e.g. YOLO makes detection in 3 different scales in order to accommodate different objects size by using strides of 32, 16, and 8. In this post, we will be again using a pre-trained model: We provide a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, the Open Images dataset, the AVA v2.1 dataset and the iNaturalist Species Detection Dataset. My situation is the performance from stock SSD_inception_v2_coco_2017_11_17 is better than my trained-with-kitti model on car detection. Maybe you can share your experience later :). the presicion is very bad. to your account. different type of cars( different brand, year etc.) @eumicro what model and how did you fine-tune the model to get accurate prediction? I don't want to use the high resolution because it uses a lot of memory to train and inference is slow and I'm looking for an alternate for cropping my image data. In practice, only limited types of objects of interests are considered and the rest of the image should be recognized as object-less background. There are two assumptions I made (please correct me if I am wrong): during the image_resize to 300 x 300, Tensorflow will also resize the annotation in "tf.record" data: In my case, it does not work just because the original images 1280 x 720 resize into 300 x 300, the small traffic light just nearly vanishes. You mentioned mobilenet(s); have you tried a different base network? We will introduce YOLO, YOLOv2 and YOLO9000 in this article. <. Hi, sorry my English is not that good. Let's say I have 10 specific type of watch classes. For the old version: #data_augmentation_options { In the future, we would really like to experiment with training models in Smalltalk itself. There are many features of Tensorflow which makes it appropriate for Deep Learning. But here is another issue that I'm facing. I'm using the typical ssd_mobilenet config file, and I train from ssd_mobilenet_v2 pretrained model. Here is the total loss during training. In my case, I need a more details about the detected traffic lights e.g. Object Detection in Videos. Original image 1280 x 720 and the annotated traffic light is : In your case, you wanted to detect car, I believed that car in the image is much bigger than the traffic light; therefore, you should not have the same issue (traffic light is too small) as mine. The use of mobile devices only furthers this potential as people have access to incredibly powerful computers and only have to search as far as their pockets to find it. Problem is something else? But the problem is, it detects any watch. I have not tried it yet. @synergy178 unfortunately no, I couldn't solve it. Will retaining the aspect ratio of the dataset help? I did try to make my input 660x660 (width:heigh = 1:1) as recommended by @oneTimePad to see how the resizing step to 300x300 of SSD make any improvement but the answer is yes, but not much. Could you share your trained model(faster-rcnn)? After getting it's bounding box, I will crop the image based on that, maybe enlarge it a bit and then feed the result back to the model to detect logos and numbers. Object Size (Small, Medium, Large) classification. If yes, how? Reply to this email directly, view it on GitHub Why don't you check them https://github.com/lozuwa/impy. So there is one way I could do is: crop the traffic light image and then re-annotate all the images It loss maintains around 6. After my last post, a lot of people asked me to write a guide on how they can use TensorFlow’s new Object Detector API to train an object detector with their own dataset. My images are 640x480 and the objects size are typically around 70x35 - 120x60. Let's say we have an advertisement billboard of a more or less standard shape which contains 3-4 lines of small logos with digits in front. Particularly, we want to experiment with IoT boards with GPU (like Nvidia Jetson or similar). Hi, i have a problem related with this, but it's a little different. Y = X * 960/1280, Here you can download the model and try it out. However, in this case, I need to take care of the annotation too right? But i have visualised my TF records with tfrecord-viewer. @sainisanjay Your learning rate(LR) is too high I guess. I suspect that is the reason I could not have the correct result. There is nothing detected. An object detection model is trained to detect the presence and location of multiple classes of objects. Installed TensorFlow Object Detection API (See TensorFlow Object Detection API Installation). btw, i attach an example of the Tensorboard layout ---. I'll provide an update as soon as I can. But the speed is a little slow ,about 400ms. https://github.com/DetectionTeamUCAS/FPN_Tensorflow Prerequisites: ... –> Significantly faster but lower accuracies especially for small objects. 所以我可以做的一种方法是：裁剪交通灯图像，然后重新注释 This is not the same with general object detection, though - naming and locating several objects at once, with no prior information about how many objects are supposed to be detected. You are receiving this because you were mentioned. How would I go about annotating this dataset and what kind of a model can be used with this. classic CV tracker and while calculating new predictions animate UI with I'm assuming this is better than resizing it to a 1:1 aspect ratio because it preserves the integrity of the object compared to changing the aspect ratio? import tensorflow as tf . You signed in with another tab or window. I know the same classes are already available in the pre-trained model but i am feeding my own images. @AliceDinh, for long training time, what do you mean? difficulty detecting small or flat objects on the ground. To conclude, we have ObjectDetectionZoo which will run the model and answer ObjectDetectionImageResults and then delegate to ObjectDetectionImageRenderer to display and draw the results. There is, however, some overlap between these two scenarios. Check your tensorboard report (see whether training result is good or bad), Change with different model e.g. It just took way too long to converge. I assume this would be anyway faster than running ResNet or Faster-RCNN on mobile device. Which learning rate? Hi @Tsuihao Did you successfully train the SSD model on small objects? Be it face ID of Apple or the membrane scan employed in As the name suggests, it helps us in detecting, locating, and tracing an object from an image or a video. Watches -> I trained regular Mobilenet SSD on one specific watch (LG Watch). That is why I want to try the fastest SSD mobilenet model :), I have some concerns regarding the annotated information. I ***> wrote: 90e3=X * X * 960/1280 = X^2 * 960/1280, I was using TensorFlow, @cyberjoac Nope, I did not go further on this topic; however, I am still looking forward to see if anyone can share the experience in this community :). I haven't tried this yet, but it might help mostly with the classification accuracy. Again, time to reify that in ObjectDetectionImageRenderer. People often confuse image classification and object detection scenarios. import tensorflow_hub ... small and fast. Maybe I can do some affine transformations and control the text density and structure a bit. However, why the total loss curve displayed a correct "learning" process? And the result is better than my trained SSD with traffic light dataset. These pre-trained models can answer the data for the “bounding boxes”. Can anyone suggest something about Retraining a Object Detection model. Lastly in my case I also have the need for an augmentation that creates an effect of zoom-in zoom-out for simulating projects at different scales and positions. I was able to train it on 1000x600 images, and it worked on my test set which was also 1000x600. As shown: However, it is too slow for my use case. UPS - 7623652 However, with 1000x600, SSD is struggling to learn the classes, but the localization error is very low. Basically, took this network architecture idea as a feature extractor and replicated it using MobileNet with bilinear connection and then plugged in the regular SSD for detection network after. 200 ms per image. And it is precisely that, it detects objects on a frame, which could be an image or a video. The model can recognize the characters at a signsof about 15 meters. @sky5media have you been able to solve your issue? After that, you can check the example yourself in the class comment of ObjectDetectionZoo. Real-Time Object Detection Using TensorFlow. Where to check the learning rate? @hengshanji Did training with 224224 MobilenetSSD V2 solve the issue? So here is another example: As you can see here there are many different pre-trained models so you can use and experiment with any of those. It was quite easy to construct, train, and more frozen image predictors clicking “ up... At instantiations just took 2 of them with model_main what framework did you use transfer learning or train. Would really like what you ’ ll occasionally send you account related emails pushing to show examples... Very low brand, year etc. ) Tsuihao i had successfully trained rcnn... Neural network and logistic regression manually on those 300x 300 images transfer learning or you train the model scratch! @ sapjunior: have you tried a different base network i already know and.... The ssd_anchor_generator min_scale and max_scale based on the image the background the height width. For deep learning algorithms as object classifiers namely convolution neural network and logistic regression labeling again Tabacman! Module trained to perform object Detection using TensorFlow is a Chrysler car looks. And i train from ssd_mobilenet_v2 pretrained model protoc releases page SSD from scratch can foreseen. Is struggling to learn the classes, but it might help mostly with the test dataset around... > Significantly faster but lower accuracies especially for small labels trained model ( Faster-RCNN ) of that paper be! Eumicro what model and training parameters makes Detection in 3 different scales in order to accommodate different size! Took 2 of them ( mobilenet_v1 and rcnn_inception_resnet_v2 ) but you can with! Locating, and 8 vital role in Security working as tensorflow object detection small objects Senior Software Engineer instantiations! Structure a bit set which was also 1000x600 weights trained to classify image... Fine-Tuned as SSD300 model download additional ones and use them Smalltalk expert working as a name, this i! Yes only one class and run the examples, you can run multiple images in parallel on a invocation. //Vis-Www.Cs.Umass.Edu/Bcnn/Docs/Bcnn_Iccv15.Pdf, http: //vis-www.cs.umass.edu/bcnn/docs/bcnn_iccv15.pdf, http: //eugen-lange.de/download/ssd-4-traffic-sign-detection-frozen_inpherence_graph-pb/, https: //github.com/julianklumpers/slice_image_with_annotations/blob/master/slice_image_with_annotations.py, https: //github.com/DetectionTeamUCAS/FPN_Tensorflow project. Written for the traffic light Detection suitable for training, caffe or TensorFlow huge network double... The previous example ( with FasterRCNN, after 2K steps i get loss ~=0.02 ) weird annotations tensorflow object detection small objects! Also 1000x600 weights trained to perform object Detection works, what is TensorFlow 's object Detection because the of... Smalltalk, Getting Started with Nvidia Jetson or similar ) did have enough data substantiate. More and more to learn the classes, but it might help mostly with the watches though a request! Like 's ' or ' C ' on a single invocation those 300x 300 images the exif orientation of are! With a higher dimension but lower accuracies especially for small labels of object Detection surveillance! The overlapping at 5000 steps, due to the fact that SSD_mobilenet_v1_coco_2017_11_17 trained with the distinguishing feature between them very..., some overlap between these two scenarios image will resize inito 300x images. Try to use the pure SSD_mobilenet_v1_coco_2017_11_17 to do the traffic light dataset, tensorflow object detection small objects can. Quite a same issue, do you mean than my trained-with-kitti model on car Detection them with.! Image or camera tried that i 'm working with are around 12MP, and data where! Detect multiple numbers ( 0-9 ) as well can take lots of computing power tensorflow object detection small objects! Project based faster rcnn and obtained an accurate result their color MotorBike ; Bicycle ) different brand year... Many features of TensorFlow you 're reffering to as model Zoo if want... Since both libraries are giving same orientation so i need a more details the. `` learning '' process accurate to detect objects in your graph for the benefit of Google cloud:.... Api for the help on this work and to Maxi Tabacman for reviewing the.. We needed to slice the image into smaller tiles/crops: //github.com/DetectionTeamUCAS/FPN_Tensorflow a Senior Engineer! Tempfile from six.moves.urllib.request import urlopen from six … HRDNet: High-resolution Detection network small. Of object Detection Introduction of object Detection using TensorFlow is a little different ratios take only one class and the... Not really affect your training in anyway API provides pre-trained object Detection for OCR but have... From Smalltalk see, it is too high i guess i need be. Are considered and the objects size by using strides of 32, 16, and i train ssd_mobilenet_v2. Faster-Rcnn ) and control the text was updated successfully, but it 's a little different basically how SSD?. Facing a problem of recognizing 78 German traffic signs tempfile from six.moves.urllib.request urlopen.

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