The Best Unrestricted List Building Strategies Report

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Input is a tight greyscale face bounding box derived from face detector output without further alignment. As of February , dlib includes a face recognition model. This model is a ResNet network with 27 conv layers. It's essentially a version of the ResNet network from the paper Deep Residual Learning for Image Recognition by He, Zhang, Ren, and Sun with a few layers removed and the number of filters per layer reduced by half.

The network was trained from scratch on a dataset of about 3 million faces. This dataset is derived from a number of datasets. The face scrub dataset , the VGG dataset , and then a large number of images I scraped from the internet. I tried as best I could to clean up the dataset by removing labeling errors, which meant filtering out a lot of stuff from VGG. I did this by repeatedly training a face recognition CNN and then using graph clustering methods and a lot of manual review to clean up the dataset.

In the end about half the images are from VGG and face scrub. Also, the total number of individual identities in the dataset is I made sure to avoid overlap with identities in LFW. The network training started with randomly initialized weights and used a structured metric loss that tries to project all the identities into non-overlapping balls of radius 0.

The loss is basically a type of pair-wise hinge loss that runs over all pairs in a mini-batch and includes hard-negative mining at the mini-batch level. The code to run the model is publically available on dlib's github page.


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From there you can find links to training code as well. We followed the unrestricted labelled outside data protocol using our in-house trained face detection, landmark positioning, 2D to 3D algorithms and face recognition algorithm called Aureus. We trained our system using 3 million images of 30 thousand people. Care was taken to ensure that no training images or people were present in the totality of the LFW dataset. The face recognition algorithm utilizes a wide and shallow convolution network design with a novel method of non-linear activation which results in a compact, efficient model.

The algorithm generates byte templates in milliseconds using a single 3. Templates are compared at a rate of We followed the unrestricted labeled outside data protocol and built our face recognition system. We collected a dataset from Internet with 4 million images of more than people, which has no intersection with the LFW dataset. We trained only one CNN model with Resnet, and use the Euclidean distance to measure the similarity of two images.

In test, we process the original LFW images with our own system. We followed the unrestricted labelled outside data protocol to build our face recognition system. We collected about 5 million images from internet of more than 50 thousand individuals. These images have been cleaned, so the dataset has no intersection with the LFW. We trained only one ResNet network. In test, we used original LFW images.

After processing these images with our own face detection and face alignment, we flipped every face horizontally. The model of our system is a single straightforward deep Convolutional Neural Network with 95M parameters before compression, embedding dimension is Multi-loss is used to enhance discriminative power of the deeply learned features during training process. We removed individuals intersects with LFW, and cleaned the labeling error by repeatedly graph clustering, some efforts of manual cleaning up has also been made.

In result report procedure, we strictly calculate average accuracy for every set using the best threshold from the rest of 9 sets on folds. Given Face pairs are compared by cosine similarity distance after feature embedding. We follow the unrestricted, labeled outside data protocol. Three different models were trained on our own dataset, which contains about 10, individuals and 1 million face images no overlapping with LFW subjects and images. The face is represented as the concatenation of these feature vectors. We used original LFW images and processed all the images with our end to end system in test.

We followed the unrestricted labelled outside data protocol. There was no intersection of LFW with training dataset. We use efficient feature maps and joint triple loss function due training which results in very fast and fairly accurate model. Feature extraction takes ms on Raspberry PI 3. We use database with 20, identities and 2 million images without any intersection with identities in LFW.

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We follow unrestricted and labeled outside data strategy. We trained using single deep convolution model followed by a novel feature training approach to generate discriminative feature output. Our network along with face detection run in real time on CPU. We followed the unrestricted, labeled outside data protocol. The data for training the face recognition and verification net include 4 million images of more than 70, individuals collected from the Internet, which has no intersection with the LFW dataset.

Our end-to-end system consists of face detection, alignment, feature extraction and feature matching. We use cosine distance to measure the similarity between two feature vectors and calculate average accuracy for each subset using the best threshold from the rest of 9 subsets on folds. Our system consists of face detection, alignment, feature extraction and feature matching. During test, we use cosine distance as similarity measurement. The system consists of a workflow of face detection, face landmark, feature extraction, and feature matching, all using our own algorithm.

The face matching module was trained using a deep metric learning network, where a face is represented as the concatenation of the 13 feature vectors. We followed the Unrestricted, Labeled Outside Data protocol. The ratio of White to Black to Asian is in the train set, which contains , individuals and 2 million face images. We spent a week training the networks which contain a improved resnet34 layer and a improved triplet loss layer on a Dual Maxwell-Gtx titan x machine with a four-stage training method.

We collected a 2 million images of more than 80, individuals from the Internet,which has no intersection with the LFW dataset. We trained a single ResNet-like network with softmax loss. We only used one model to extract the features. Cosine distance is used to measure the similarity between two features. The training dataset contains , individuals with 10 million face images after data augmentation no LFW subjects are included in the training set. We trained a single model with improved r CNN MB and two angle-based loss under curriculum learning.

Cosine distance is employed to measure the similarity of test features in pairs. We collected a dataset from Internet with 5 million images of more than people, which has no intersection with the LFW dataset. We built face verification system with our own face detection and alignment algorithms,which are all based on deep CNNs. We trained only one inception-resnet-like network. Using the best threshold from the rest of 9 sets on folds and the L2 distance for the given pairs comparision, we strictly calculate average accuracy of each set in final result report.

We followed the unrestricted, labeled outside data protocol for our face recognition system. Our dataset consists of 80, identities and 8 million images. It does not have intersection with identities of LFW. Our face recognition system runs end to end on deep CNN models including face detection, face alignment and feature extraction. We followed the unrestricted labeled outside data protocol and built our commercial face recognition system.

We trained only one modified Resnet model with 27 convolution layers. The output feature dimension of our model is , and the Euclidean distance is used to measure the similarity of images. We follow the Unrestricted, Labeled Outside Data protocol. We collected a dataset from internet with 3 million images of more than 60 thousand individuals, which has no intersection with the LFW dataset. Our model is a ResNet network with 20 layers, using 2 loss functions. During the test, we used original LFW images and we strictly calculated average accuracy for every set using the best threshold from the rest of 9 sets on 10 folds LFW was not used for training or fine-tuning.

Our training dataset contains about 3. We trained model with multi-loss and then finetuned by tripletloss. One model achieved We concatenated feature vectors and calculate the Euclidean distance of testing images as similarity measurement. The final mean accuracy is We collected 8 million face images that include k individuals, and remove the overlap with LFW faces, then we aligned and rotated, cropped the face to gray images for training the 27 layers resnet like CNN.

Hi Brian, This is just a wonderful list to build a useful email subscribers. Thanks for sharing this valuable info! Brian, I just found you and this post was awesome. Thanks for all the great info. I look forward to learning more from you in the future. Do you have an app for that? Good question, Cary.

I think Buffer may be your best bet. You can load it up for a year I think…. Brian, You provide high quality, relevant marketing tips bro. Very useful content! I have tried to access the link to the Site Alerts website, but it seems that the domain is no longer active. I think it is very useful tool for any starting entrepreneur out there, so I am very interested in following this.

Does anybody have any idea what happened to them? Thank you for providing added value by your articles and keep up the good work! Thanks Olivia. Feel free to use that instead: BuiltWith. Some brilliant tips here. Email Marketing is a gold mine and a great way of connecting to new visitors. Thanks for sharing your outstanding work. Yet another kick-ass post, Brian. Hell, im gonna try and get my grandma hooked on your blog is never too late, right? Whatever you share gives a lot of quality to our knowledge. Its a wonderful list. Right now i am working on the 17 untapped backlink techniques, will start working on it once i am done with it.

Great post Brian. As someone that has stupidly not concentrated on building my list till recently I found this post invaluable to get my list building on the right track. Thank you for this informative and easily understandable post. Bookmarked and shared. Another crazy good post! Man your content is so good. I had a request from your fans. Would you consider doing a post on http: vs https? Will that change to this new format hurt our backlinks and create s?

Is it necessary to change? How to go about doing it? Everybody is talking about this new google change and how important it is, but NOBODY is talking actionable steps and what to do. You are the man of action and actionable steps. So I wanted to make that humble request for your consideration as I think it would make smashing content for your site.

Since, this is fresh and a public hunger for that content I think it would generate you a lot of backlinks because of the viral nature. Shameless I know….. But hey maybe it will get your fans to keep submitting ideas.


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There might be something here to channel your fan base to get us to help your business. Think it over….. You really have reached Malcolm Gladwell Tribe status. And have done a great job with that. At this point whatever you asked the tribe to do they would deliver for you. I just have to implement it myself first. I was thinking to enlarge my subcriber base from a very long time but was not able to due to very less traffic.. But after following the above mentioned techniques, I think it could be a boon for my blog. Brian , your every post is like a book, I always read your post and try to find a few questions to ask..

I wonder how long you take to prepare a post like this, I probably would take a whole year! Really impressive link building guide that helps me to improve the current link building process of my own website. I will follow your steps and guides for my projects. This is amazing stuff! Thank you so much. I have a question for you. You use Aweber as your email service right? Is it LeadPages? I find that Awebers sign-up forms are not very good. Thanks, Ryan. Wow really awesome list Brian! Now I understand the effectiveness of using a proper statement on the popup optin form.

Now I had to rewrite something Interesting making user feel they are missing something if they not going to subscribe! Some will just throw an email in there to get them to go away. Hi David, you raise a good point. My feeling is this: a mediocre lead is better than no lead at all. If a business has issues converting targeted people to sales, then the sales process needs to improve. Pop-ups are two-sided for me. On the other, some folks just find them annoying.

An enjoyable and informative post nonetheless. Thanks, Brian! Awesome stuff. I am using aweber and optimise press at the moment. I might of missed it if you mentioned it.. Will definitely test the pop-up opt-in. Will give a shot to another methods too. I was skeptic at first, but reading your logic behind it, it makes sense. Thank you, Brian. Sounds good, Al. Definitely try it and let me know how it works out for you.

Excellent post as always Brian. I may have lost that email.

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Would it be possible for you to post the download link here please? Thanks in advance. Thanks Angelo. If people sign up via that opt-in form, they will get a beautiful PDF file that summarizes that post. What should I do to make sure that people who sign up thru that post-specific opt-in form will automatically be added to the main sidebar list? Hi Frank, that depends on which email provider you use. I recommend reaching out to them with a link to this post and ask them how to set it up.

Lots of great resources. Right now I use it for my music, but as I build my new business this is putting me ahead of the game. For proof, I dare all readers to scroll down the comments section and see how many comments get a Brian Dean reply. Hint: all of them. Including this one, I bet.

This is such an exhaustive post. And it is going to take exhaustive efforts to implement. But that has affected our conversions. Hi Brian, Excellent post! This happened to be my introduction to your content. Can you share which tool you used for the confirmation page, which pops up after clicking for the PDF checklist? I saw that you use Max Blog Press, but I think that was an answer to a related, but different question. Very very good stuff Brian. And my attention span these days is quite brief. Thanks for the great tips! Interesting ideas here. I found a couple things I had not heard of.

I have subscribed to your newsletter as soon as I confirm. Mayank, thanks. Hi Brian! As I see I miss several list items on my blog… want to implement immediately! One question though — with the yellow box subscription after the first paragraph I subscribe and get the pdf opened at the same window…. And i want to try 1 more option — with timed popup leadboxes or exit leadboxes to see what I get Thanks a lot!

Your blog is a real treasure!!!! Sounds good, Alena. These should definitely help. This list is brilliant! Your usual top-notch quality in action. Nowadays there are various free plugins that do what subscriber magnet does and then some. I thoroughly enjoyed the post. It is a lot to absorb at once but I will be reviewing the information later and take the appropriate actions necessary. It was an opener for me on the very many and simple strategies existing to help with traffic and interactions. I love your site and have signed up for your newsletters. Hey Brian, Love this post! Been using it as a bible for our list building planning.

You made me laugh throughout the post by this single line. Extremely helpful post! Thanks for this! Wow — amazingly in depth post on list building! I am having a real focus on this just installed my lead magnet which is doing well but there is a tonne of things I still need to focus on. Thanks for the tips. Thanks Emma. Excellent stuff. I used some of the strategies on my blog, and am thinking I should implement some more strategies listed here. Great blod post.

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Some really fantastic marketing strategies here. Perhaps surprisingly email converts better than any other channel such as facebook or twitter and pop-ups have been proven to collect the most email address of visitors. Been reading you for a couple of weeks now. A lot of people speak quite highly of you. I have been using SEO techniques to help my local business to great success, but I love learning new tools and tips that I could possibly implement in my business SEO.

I truly appreciate the About Me page and turning it into a lead gen landing page. I think I am going to have to try that. Thanks a lot! Hey Brian, have you found other opt in providers that have the 2 button optin like bounce exchange? I agree with you and do the same.

However, from January 10, Google promises to start punishing the mobile sites with pop ups. In your opinion, how it will affect ecommerce sites and pop up providers? What an amazing amount of awesome content. And thanks for making this video that shows you explaining what you did to rank the page. Love your work, keep it up. Maybe its not all as complicated as you suggest. Hey Brian! Big fan here! Those would be the animations that lets say, as you scroll down, the content reveals with certain animation fade, slide, etc , or is it better to just use static pages?

I had on my website animations like that, that made it look better, but removed them coz of conspriacy. You can remove my previous comment, I pressed Enter by mistake and it posted while not being finished. Yes you have ranked on 1st position! Hey Brian I was watching ur videos which u left 2 weeks ago seo case study as u said list building keyword ranked out 1 in your video. You are the true seo guy I watch every video to be frank I still not subscribe ur youtube channel and I will do it now.

Big fan here of you! Thank You deer. Great Read! I have already started to implement a few. How can I add the subscribe to email checkbox in a comments? Been looking for weeks! So much info that you give Brian. Just Bookmarked so much. Very Appreciative and I know this will help as well. Your email address will not be published.

Leave this field empty. In fact: The strategies in this post helped me grow my list from scratch… to , subscribers. Click a section below to be taken right to one of the strategies. Use Content Upgrade Popups 2. Limit The Number of Options 5. Use BuiltWith. Use a Blog Post Teaser Use Weird Call to Actions on Buttons For example, a while back I decided to try using a popup.

In fact, that simple-looking popup added over new subscribers to my email list in 3 months. Never heard of the The Content Upgrade? No worries. For example: In my Google Ranking Factors post , I give away a checklist that makes the information from that post much more actionable: And then it hit me: Why would I offer someone something VERY specific with The Content Upgrade… …and then turn around and make a generic offer in my popup? The result? Speaking of a strategy that gets results right away… Tweet this List Building technique 2.

The University was looking to increase conversions on their email updates page, which you can see here: Instead of using Qualaroo to poll their visitors, they had the tool encourage people to sign up to their email newsletter: In fact, they set it up so you can literally subscribe directly through the Qualaroo form! Did it work? In a word: YES!

Sometimes they change their mind. Sometimes the dog poops on the rug and they forget to confirm. Now for some good news: You can significantly boost your subscriber numbers by getting more of your opt-ins to actually confirm their email. How can you do that? Optimize your confirmation page. First, I re-iterate the benefits of subscribing. Free updates, strategies and tips…or subscribing to a newsletter? Thought so. Second, I include a strong call to action that asks them to confirm right away.

Limit The Number of Options Which sounds better to you? Making millions from the stock market… …or being lazy and eating Cheetos on your couch? Although each Bounce Exchange popup has a unique design, they have one thing in common: They make you feel like an idiot for not opting in. Bottom line: Including a second option even a silly one will convert more people into email subscribers.

Tweet this List Building technique 5. How is that going to boost social proof? You need social proof to get subscribers. But you need subscribers to get social proof. But how? Good luck with that Or you can use this little-known tool called Built With that shows you the EXACT tools your competition uses to build their email list. How does it work? Pretty awesome, right? Tweet this List Building technique 7. The 2-step LPF technique is very simple. First , you identify landing pages that convert well on your site. Second , you design your site to funnel people to those high-converting pages.

So for me, my Social Squeeze Page and my Newsletter page are my top 2 best-performing pages.


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Next, I added links around my site that point to those two pages. Tweet this List Building technique 8. And where does that link in the email lead to? You guessed it: A well-designed landing page. Tweet this List Building technique 9. Direct mail marketers have used these words for decades to increase the perceived value of their offers… …and you can do the same. How can you do the same thing? First, think of a giveaway that viewers of that video might be interested in.

Next, add a card to your video that stands out. Tweet this List Building technique Skipped that one? Go back and check it out. I may add it again soon You can easily set this up yourself: Just create a high-quality report, ebook or checklist. Then, give your resource to subscribers right after they sign up. The question is: How can you leverage fear of loss to build your list?

I had an announcement at the beginning… …and end of the post: Those are OptinMonster links. When someone clicks on them, an opt-in box appears that highlights the potential loss of not subscribing: So how has this strategy performed? Well the two links from that one post very quickly added 37 new subscribers to my list. Not bad at all for a few minutes of work. So you click away, never to return. Not good. Well there is. And it only takes a few minutes to set up. Why does this work so well? In fact, my About Page converts at 9. I believe he is going to be on Fox business tonight….

Stole this trick from Neil Patel P. Hello Brian! Wonderful information very challenging and educative!! Another great post, Brian! Thanks again! Another Gem from backlinko treasure chest. Thanks Rohit. The About page strategy has been working wonders for me.

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