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Among the 19 registered teams, 14 teams have successfully finished the task during the evaluation time window. Посетить страницу источник your recognizer has encountered issues during the first round of evaluation. Please ping us for another round of request.

We can send you up to 3 rounds of evaluation trafficas long as time allows. But keep in mind, you can only choose one of the results for ranking. By default, we microsoft project 2016 training manual free use your last result.

More teams passed dry run format, speed, and accuracy and are ready for final evaluation. Latest update about dry run progress is shown below. We are conducting dry-run right now. Here is the current progress:. Dev data set is being sent to all recognizers in next microssoft days.

We will retry at 1 hour interval until all the images in dev set got recognized. Please keep your classifier running. The develop set will be sent to your recognizer to help you verify:. As before, we will use an open multimedia hub Prajna Hub for evaluation. That is, you need to register your recognizer to Prajna Hub, which essentially turns your recognition program to a cloud service.

/13987.txt your algorithm can be evaluated remotely. Note that your algorithm is still running on your local machine and you have full controls on it.

This update provides:. You are expected to see the response like this: pinged xxxx and verified classifier xxxx is смотрите подробнее, you can now send test request to it. You are expected to see the response in this format: Response: tag Thanks to the advance microsoft project 2016 training manual free deep learning algorithms, great progresses have been made in visual manial in the past several years.

But, there is still a big gap from these academic innovations and practical intelligent services, due to the lack tarining 1 real world large scale data with better quality for training and evaluation. To further motivate and challenge the academic and industrial research community, Microsoft is releasing MS-Celeb-1M, a large scale real world face image dataset to public, encouraging researchers to develop the best face recognition techniques to recognize one million people entities identified from Freebase.

In its V1. This year we will focus on face recognition task. The contestants are asked to develop image prlject system based on, but not limited microsoft project 2016 training manual freethe datasets provided by the Challenge as training data to recognize 1M celebrities from visual 2012 professional product crack free face images.

The 1M celebrities are obtained from Freebase based on their occurrence frequencies popularities on the web. Grounding the face recognition task to a knowledge base has many advantages. First, each people entity on Freebase is unique and clearly defined without disambiguation, making it possible to define such a large scale face recognition task. Second, each entity naturally has multiple properties e.

The measurement set consists of celebrities sampled from the 1M celebrities, and for each celebrity we have manually labeled up to 20 images scrapped from commercial image search /14414.txt. But the identities of these celebrities will not be disclosed, so that the contestants cannot optimize just for these celebrities. To microsoft project 2016 training manual free high recognition recall and precision rates, the contestants will have to develop a recognizer to cover as many as possible celebrities, which will be of great value microxoft help advance the state of the art in face recognition.

A contesting system is asked to produce 5 or less labels for a test image, ordered by confidence scores. Top one accuracies will be evaluated against a pre-labeled image dataset, which will be used during evaluation stage.

Microsoft Cognitive Servciesetc. We will detect and ban the contestants for this kind of behavior. The 1M celebrity names and about 10M face них windows 10 home button not opening free Рождеством with labels will be provided to the participants for data filtering and training.

A development data set, which contains several hundreds of face images and ground truth labels will be provided to the participants for self-evaluations and verifications. Please note that above datasets are all optional to be used.

That is, for N images in the measurement set, if an algorithm recognizes M images, among which C images are correct, we will calculate precision and recall as:. Note that we also add distractor images to the measurement set. This will increase the difficulty of achieving a high precision, but is micdosoft closer to real scenarios.

Note: We will evaluate recognition algorithms on two different tracks:. This track encourages participants to develop robust algorithms with good generalization capability. For DevSet2, the test images are randomly selected, which are highly likely to be covered by the training data, if that entity is covered.

In this way, the overall coverage of your algorithm, i. This track encourages participants to collect and use as many as training data as possible, and focus on the scalabilities. Evaluation Platform: An open trainong hub, Prajna Hub, will be used for the evaluation, which читать далее turn your microsoft project 2016 training manual free program to a cloud service, so that your algorithm can be evaluated remotely.

Similar methodology has microoft used in the last several IRCs and it was well-received. This time, we made it even easier, with extra bonus including:. The Challenge is a team-based contest. Each team can have one or more members, and an individual microsoft project 2016 training manual free be a member of multiple teams. The team membership must be finalized and submitted to the organizer prior to the Final Challenge starting date.

At the end of the Final Challenge, all entries will microsoft project 2016 training manual free ranked based on the metrics described above. The top three teams will receive award certificates. A: Yes, you can use pre-trained CNN models by ImageNet or other datasetas long as you can fine tune узнать больше здесь well to fit for the dog breed categories. Basically, we treat these pre-trained CNN models as feature extraction layer.

Is this expected? We intentionally to NOT remove these 15K images from the data. A: We extracted all the dog breeds that have matched names in pairs in Clickture Full, which result in dog breeds in Clickture-Dog data set. The evaluation dataset will include categories, including part of these dog breeds which have more than samples in Clickture-Dog dataset. But we will also include a few categories with small number i. This is to encourage participants to train a recognizer that can recognize as fre as possible dog breeds.

The Clickture-Dog is just one way to collect training data. You can also filter the Clickture-Full to find more data. This year, we also allow participants microsoft project 2016 training manual free collect training data outside of Microsoft project 2016 training manual free by themselves, but we will only control the evaluation set to 201 the problem. You can also use this website to traijing this base64Encoded image data and save it to a jpg file manually. Q: We are trying to run the sample server on our Linux machine and we are microsoft project 2016 training manual free an Unhandled System.

Please note that the sample code has to be run with the newest version of fsharp 4. The default mono and fsharp packages you get from Ubuntu may not be mivrosoft. Please follow the readme file provided with the sample codes to install the newest mono and fsharp package. If you have already install the old version, please do the following to fix the problem.

Q: When we use CommandLineTest tool, we encountered error message when checking the the availability of our classifier:. A: Some teams confused serviceGuid with providerGuid, so they used the providerGuid to hit a classifier in step 2, which should be serviceGuid instead. ServiceGuid is used to identify your classifier, while providerGuid is used to identify your team.

No need to change anything. Share this page:. More data coming soon Expand all Collapse all. We will announce team member list shortly. A two-stage training strategy was used. Firstly, 27, classes each with over images a total of 3, images were choosed to train the network in the first stage. Secondly, we sampled 50 images from those classes with over 50 images due to time limitation and retained all the images of other classes a total of 4, images of 99, classes. Only the last fully-connnected layer как сообщается здесь finetuned.

All the experiments were done in the last week. Beijing Faceall Technology Co. Starting with an initial label deficient face image training set, we iteratively training a deep neural network and using this model for sampling the examples for further manual annotation. We follow the active learning strategy and derive an Value of Information criterion to actively select candidate annotation images.

During these iterations, the deep neural network is incrementally updated. Secondly, we leverage the trainijg of MSR Image Recognition frree to a cross-domain retrieval scheme. To achieve real-time retrieval, we perform the k-means посетить страницу on the feature space of training data.

Furthermore, in order microsofg learn a better microsoft project 2016 training manual free measure, we apply a large-scale similary learning on the relevant face images in every cluster. Compared with a lot of existing networks of face recognition, our model is lightweight and our retrieval method is also promising. In this framework, a novel microsoft project 2016 training manual free pairwise ranking loss is traning utilized to help alleviate the adverse influence from noise data.

We also design an online algorithm to select hard negative image triplets from weakly labeled datasets for model training. Experimental results on MS-Celeb-1M dataset show the effectiveness of на этой странице method.

The celebrity recognition is treated as a classification problem. Our approach is based on the Deep Residual Network.