Darknet cnn mega

Вопрос - применять ли активацию меж DW и PointWise свертками. Xception говорит что активация усугубляет результаы, но с иной стороны статья написана в м и с тех пор все поменялось. Separable Convs в голове EffDet тоже без нормализации и активации меж ними, но так изготовлено поэтому, что эта голова применяется к различным фичемапам и нормализация там отдельная для каждой мапы, меж SepConv.

Остальное достигает наиболее долгой тренировкой и подбором гипер характеристик. Этот опыт нужен только для бейзлайна и измерения свойства дальнейших модификаций. Ассоциировать необходимо с exp Эта модель приметно лучше. При том в оригинале есть и downsample in bottleneck и not linear bottleneck и stem ужаснее. С ходу решил вставить туда много конфигураций в архитектуру:. Мысль для таковой подмены была в избегании dimensionality reduction по максимуму.

Работает чуток лучше, но все равно ужаснее чем изначальный вариант. Кажется что linear bottleneck для данной нам модели работает приметно ужаснее не глядя ни на что. Этот вариант еще поближе к начальному. Работает ужаснее чем exp33, но может быть это соединено с наименьшим количеством характеристик.

Работает приметно лучше чем exp28 и Кажется что дело в количестве активаций и обычной голове. Работает ужаснее чем дефолтная версия GENet не чрезвычайно понятно почему, но не сильно ужаснее. Обе схожие по скорости, хотя D53 слегка жирнее. CSP вариант уменьшает количество характеристик, но остаётся таковым же по скорости.

Охото сделать лучше лосс для классификации, привнеся идеи из metric learning, чтоб the maximal intra-class distance is smaller than the minimal inter-class distance under a certain metric. SphereFace: Deep Hypersphere Embedding for Face Recognition apr v1, jan v2 Решают задачку open-set face recognition то есть набор людей в test set и train set различный.

Дефолтный лосс - softmax cross-entropy , которйы учит separable features that are not discriminative то есть границы классов пролегают чрезвычайно близко друг к другу. Center loss -добавляет доп лосс за отклонение от центра классов, но ежели классов много, центры недешево и долго считать. Еще почти все остальные работы употребляют euclidian distance для разделения классов, но это не совершенно, поэтому что softmax naturally раскидывает фичи по углам в статье есть матан почему , предлагается это применять.

В отличие от A-Softmax дают large margin cosine loss LMCL , который просит разделимости не в угловом пространстве, а в косинусном, отлично решая первую делему A-Softmax. Картина ниже из ArcFace, в статье CosFace есть схожая, но там создатели некорректно нарисовали границы для cosface, они нарисовали их прямыми, хотя на деле они искривляются из-за природы косинуса.

Далее создатели разъясняют почему принципиально дополнительно нормировать фичи A-Softmax нормирует лишь веса. В отличие от cos face дают добавлять margin в угловом прострастве, а не косинусном. Весь способ в одной картинке. ArcFace неплох, но у него нет штрафа за intra-class distance, то есть он начинает сжимать классы лишь ежели поставить довольно огромное значение margin.

Мысль - употреблять адаптивный margin, который будет зависеть от расстояния до наиблежайшего центра класса, чем больше расстояние, тем больше margin, чтоб intra-class не расползались очень сильно. Центры класов очен дешево получаются из матрицы весов крайнего нормализованного слоя.

Опыты указывает, что это улучшает качество по сопоставлению с ArcFace. Не плохая статья, которая поднимает принципиальный вопросец про наказание за большой intra-class, но может быть это можно делать элегантнее? Мои мысли: Идею CenterLoss вначале выкинули из-за того, что там трудно было считать центры, но в случае с ArcFace центры это строчки матрицы.

AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations Angular способы это естественно отлично, но у их аж 2 гиперпараметра, причём performance чрезвычайно чувствителен к обоим из их. Ежели s очень небольшой - будет наказывать даже при чрезвычайно небольшом угле, ежели очень большой - не будем наказывать даже при чрезвычайно большом угле.

На что влияет m : на малый угол, опосля которого лосс начинает падать. Ежели пристально поглядеть на картину снизу, видно что s влияет на ширину кривой, а m лишь сдвигает её. Далее следует мало матана и доказательств, вывод которых - для неплохого подбора и оценки S необходимо знать сумму остаточных членов в знаменателе softmax то есть всех тех что не в числителе. Видно что в течении обучения, S миниатюризируется, адаптивно усложняя задачку для сетки.

Кмк чисто win, нет гиперпараметров да еще и адаптивно всё подстраиваем. A Discriminative Feature Learning Approach for Deep Face Recognition aka Center Loss Статья древняя, потому всё дело происходит в euclidian space, дают считать центры для классов и добовляют доп член, который штрафует за отклонение от этих центров. A Comprehensive Study on Center Loss for Deep Face Recognition Только мельком пробежался по статье, самая основная мысль - применять generalized center loss, который не штрафуе за отклонение меньше какого-то порога.

Мои мысли: у себя в опытах я употреблял eucl заместо MSE, может быть оно работает ужаснее поэтому что стягивает близкие точки так же сильно как далекие, а охото стягивать далекие посильнее. Мысль Center Loss просто обобщается на angular координаты.

Cosine Softmax с степени экспоненты cos и Linear Softmax в степени экспоненты сам угол, этот вариант выше я называл Arc Cos. Молвят что s необходимо выбирать автоматичеки так, чтоб макс. Непосредственно эта мысль вполне из adacos, разве что отсейлили на значение в нуле. Чрезвычайно много высококачественных тестов, везде их Lin-Cos лучше чем лишь Linear либо лишь Cosine logits.

Причём Linear практически постоянно ужаснее всех, что кмк чрезвычайно удивительно. Позже еще добавляют margin angular margin как в cos face. В целом мой вывод таковой - статья не плохая, но не понятно что конкретно им докидывает по сопоставлению с cosine logit, ежели это не такое мощное затухание стягивающей силы, то может необходимо очевидно center лосс докинуть? Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere чрезвычайно свежайшая, nov Мне на данный момент Кажется, что ежели пробовать улучшить ArcCos где в степени экспоненты стоит сам угол, то добавление явного margin к этому углу будет работать отлично.

Создатели говорят, что градиенты все равно быстро гаснут и resnet это ансамбль огромного числа не чрезвычайно глубочайших сеток. В уникальной статье про ResNeXt создатели глупо зафиксировали количество групп на свертку. Не непременно употреблять однообразные блоки по всей сетке. Еще в статье есть мысли о том, как верно делать fusion снутри каждого блока, смотрится разумно, но нет замеров по скорости, а это может быть критично.

В целом статья неплохого свойства и результаты вызывают доверие. В ResNet уменьшение размерности происходит в 2-ух местах - снутри bottleneck и в отдельной ветке для residual. В статье про MixNet давали заменить 3x3 depthwise свертки на сверти 3ч3, 5х5, 7х7, 9х9. Knowledge Distillation and other distillations. Создатели берут две аугментированные рисунки, пропускают через модель и EMA модель, минимизируют cosine distance меж 2-мя приобретенными представлениями. Еще для симметрии создатели пропускают обе рисунки через обе сетки и считают сумму этих лоссов.

Supervised Contrastive Learning - до этого люди пробовали предобучать модели для image classification с помощью contrasive loss, оно работало, но была одна неувязка - contrastive loss не в курсе про классы и может расстаскивать рисунки из 1-го класса далековато друг от друга. Самое основное - непревзойденно бустит качество supervised классификации, смотрится как plug in play.

Из-за того что у нас есть лейблы, в батче может быть несколько положительных примеров, создатели изучают два варианта лоссов, демонстрируют что вот таковой работает лучше:. Лаконичный обзор: смотрим на contrastive learning CL как на dictionary заморочек, по query Q отыскать более схожий ключ K , но не в явном виде отыскать, а чтоб InfoNCE был высочайший.

Meta Pseudo Labels Создатели задаются вопросцем - как получить отличные target distribution для обучения? А давайте просто будем употреблять Meta Pseudo Labels, то есть будет еще одна малая сетка, которая будет процессить таргеты и выдавать что-то такое, оптимизация по чему будет облагораживать лосс на валидации. Есть пара имплементационных деталей, без которых не работает - необходимо учить Учителя не лишь на выходах студента, но и на GT валид.

The difference is the purpose of SS self-supervision and how contrastive task is performed. In CRD, contrastive learning is performed across teacher and student networks to maximize the mutual information between two networks. In SSKD, contrastive task serves as a way to define knowledge. It is performed separately in two networks and then matched together through KL-divergence, which is very different from CRD.

In SSKD, however, the main goal is not to learn representations invariant to transformations, but to exploit contrastive prediction as an auxiliary task for mining richer knowledge from the teacher model Употребляют вот таковой лосс для обучения contrasitve части: Teacher учат в две стадии. Финишный лосс student состоит из 4х!

KLD меж contrastive simularity matrix обоих сеток. Отмечу, что создатели кое-где употребляют нормализацию, кое-где не употребляют и везде употребляют температуру. Ассоциируют качество различных self-supervised задач типо jigsaw и rotation, демонстрируют что contrasitve работает лучше всего.

Ассоциируют много остальных KD способов, демонстрируют что CRD работает лучше всех опосля их способа естественно же. Пробуют обучаться лишь с помощью лосса 4, но у их плохо работает. В целом отменная статья, но работает имхо вопреки, а не благодаря. Плюс я надеюсь сделать из этого магистерский диплом. Базисные сети и идеи: ResNet - классика. Базисный блок BasicBlock либо BottleNeck. 1-ый еще скорее, но приметно ужаснее работает.

Дают несколько чрезвычайно общих вариантов MasterNet. Acc 1: Фаворитные архитектуры для различных режимов показаны ниже. Создатели учили свою normal модель на разрешении Провалидировал веса: Вероятные улучшения для данной нам сетки - не употреблять Inverted Bottlenecks, а просто линейно увеличивать количество каналов, используя group convs заместо depthwise upd. Доп идеи: Adjoint-Network - дистилляция прямо во время обучения. Все опыты будут проводиться на уменьшенной версии Imagenet х для наиболее стремительных итераций один опыт в таком сетапе занимает План экспериментов: Spoiler Template To be added Darknet Ежели ассоциировать с 9м, видно что linear bottleneck работает лучше.

Хотя в целом для 9М характеристик и таковой скорости не плохо. Этот вариант скорее, на 4М меньше характеристик и ужаснее по качеству. Try Lightrun to collect production stack traces without stopping your Java applications! Comments 10 by zakajd. Margin value was set to 0 weighted CE to deal with imbalanced classes, Progressive increase in image sizes reliably boosted scores Note: In Google Landmarks task was only to generate good features. Used Corner-Cutmix.

Networks learns to look at image in different scales, which is useful for real-life scenarios Triplet loss Often mentioned in earlier papers. Hard-negative mining is a standard process [6], [16] Typical architectures for metric learning: two-branch siamese [39], [40], [41] and triplet networks [42], [43], [44]. They employ matching and non-matching pairs to perform the training.

GeM power parameter is learned, but in practice close to 3. Heavier weights are put on as the rank gets higher. Use feature maps after the non-linear activations ReLU so that the elements in each feature map are all non-negative. AQE and alphaQE are analyzed and later shows better and more stable results. Discriptors for all those images are summed and renormalized and used in a new search.

Model architecture: Remove last pooling and Fully Connected layers. Multi-scale evaluation at test time. Resize images, collect features and sum them or concatenate? Embeddings are L2 normalized before computing the loss, and during evaluation. Use MAP R metric and show it to correlate with performance better, than other methods. Training and validation sets are always class-disjoint, so optimizing for validation set performance should be a good proxy for accuracy on open-set tasks.

ArcFace is best on 2 datasets and almost the best on another. See table Optimal parameters for different datasets. Trained model with some decent performance, then for each class clustered all images and took biggest group, discarding all others. Used this data for training. Another approach is to cluster all test data, assign synthetic group labels and use this data jointly with train. Slightly boosts performance due to large number of image noise. Spoiler Template To be added Пробую уйти от CE к остальным лоссам пока они всё чрезвычайно сильно оверфитятся.

Работает сильно лучше exp Spoiler Template To be added exp?? Spoiler Template To be added ИДЕИ Уменьшить вес center loss члена в exp92 добавить margin к arc-softmax и arc-softmax-center испытать сумму a-softmax и arc-softmax. Spoiler Template To be added Новейший цикл тестов, цель - узнать как применять Novograd и затестить одну новейшую аугу от Ильи exp97 бейзлайн. Running theta: 0. Running S: SE-var3 не глядя на всю заманчивость идеи оказался нерабочим exp отыскал новейший вариант attention - FCANet обзор статьи выше.

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Сайт доступен для входа в хоть какое время дня и ночи, что дозволяет никак не ограничивать его внедрение. По всем интересующим вопросцам можно обратиться в техподдержку и для вас непременно посодействуют. Купленный продукт доставляется покупателю в очень сжатые сроки, как дозволяет ситуация. Средства со счета покупателя на счет торговца переводятся за чрезвычайно маленький просвет времени.

Вы сможете входить на площадку как с домашнего ПК, так и с хоть какого другого мобильного устройства. Личная информация юзеров, а также данные о совершенных ими действиях накрепко засекречена. Зеркало представляет собой тот же веб-сайт, лишь расположенный по другому адресу. Для входа в личный кабинет используйте те же данные, что и на основном веб-сайте. Вы случаем не робот? Что такое Mega? Актуальные рабочие ссылки на веб-сайт. Зеркала Mega. Mega вход. Крутая торговая площадка в сети TOR - Mega.

Mega даркнет маркет. Достоинства Mega. Брал у Love Shop экстази, отправили нахуй, даже слушать не стали, модера никак не пригласить, так как у шопа какие-то привилегии, он сам решает, приглашать либо нет, даже клавиши нет. В Pokemon GO брал тоже экстази, ненаход, дали подмену, ненаход, начали пиздеть, что выдадут подмену, ежели я куплю у их какой-либо продукт удачно 2 раза.

Вот серьёзно, как опосля такового брать что-то на гидре? У меня вообщем опыта никакого нет, даже ничего толком не употреблял, от знакомого про мдма услышал и то мне от навязчивостей необходимо было. Но в моём случае нихуя. Кто-либо брал кое-где не считая гидры? Есть рутор, но там больше опт. И была мега, которая походу все. На новоспайсе брали? Мутноватый веб-сайт, молвят что был еще во времена рампа и переродился. Создатель пишет как солевой, но вроде норм.

Интерфейс говно. Наконец-то Мега взялась за голову. Надеюсь запилят возможность выбрать тип клада, а то не считая выбора метро ничего нет. У омг еще лютый рассинхрон с блокчейном. Транзы по часов идут. А мега — пидоры, много жалоб на дырки на адресах. Мужчины почему не раскрывается площадка? ВПН включен. Подписи есть лишь у подтвержденных хозяев веб-сайтов на Годнотабе? Подписи приобретенные до Старенькые не работают.

Mega — новейший русский маркетплейс , который работает в Даркнете. Мега по задумке разрабов обязана была заполнить пустоту опосля Гидры. И в конечном итоге у нее это вышло. На это указывают поисковые запросы в Google. Функционал Mega вполне взят с закрытой в апреле года Hydra. Разница лишь в дизайне. Кажется, все просто, но не все новенькие способны отыскать на Mega Darknet Market правильные ссылки.

Подобно Hydra, Мега обслуживает покупателей лишь из Рф и неких остальных приближенных государств. Соответственно, и наркошопы работают на местности РФ. Невзирая на то, что Mega полностью новейший проект, он уже стал объектом мошеннических манипуляций. Некие способные фишеры настраивали поддельные URL-адреса.

Таковым образом они заманивали наивных юзеров на сомнительный ресурс. А далее списывали внесенные покупателями на фейковый счет средства. Перечисленные URL-адреса работают в незапятанной сети. Это означает, что они раскроются в обыкновенном браузере. Вы увидите сообщение о этом.

Ежели вы откроете первую ссылку из перечня выше, то попадете на страничку со перечнем Mega Darknet Market рабочих зеркал. Вы сможете пользоваться автоподбором рабочего адреса на Мега. Обратите внимание на то, что onion зеркала и ссылки открываются лишь в Тор. Потому заблаговременно установите этот браузер на свое устройство.

На этом исходном шаге у почти всех появляются трудности, так как Tor совместно с официальным веб-сайтом заблокировал Роскомнадзор. Но мы поможем обойти блокировку. На самом деле ссылка на Mega Darknet Market в Тор раскрывается мгновенно. Маркетплейс работает в Даркнете, потому имеет зону.

Онион ссылки не открываются в обыденных браузерах, таковых как Mozilla либо Google. У Тор таковых заморочек нет. Итак, чтоб открыть Mega Darknet Market зеркало, пригодится Tor.

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Launching Visual Studio Code Your codespace will open once ready. Latest commit. Git stats 3 commits. Failed to load latest commit information. CNN Notebook. View code. Trust but Verify To see if your installation is correct run: conda env list This will show all installed environments with an asterisk showing your current one. About No description or website provided.

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Зеркало представляет собой тот же веб-сайт, лишь расположенный по другому адресу. Для входа в личный кабинет используйте те же данные, что и на основном веб-сайте. Вы случаем не робот? Что такое Mega? Актуальные рабочие ссылки на веб-сайт. Зеркала Mega. Mega вход. Крутая торговая площадка в сети TOR - Mega. Mega даркнет маркет. Достоинства Mega. Почему выбирают mega. Круглосуточный доступ Веб-сайт доступен для входа в хоть какое время дня и ночи, что дозволяет никак не ограничивать его использование.