Yolo Selfie Website
🛑 ALL INFORMATION CLICK HERE 👈🏻👈🏻👈🏻
Yolo Selfie Website
YOLO! This girl took secret selfies with a horse in her parents’ bathroom
Allow microphone access to enable voice search
Sometimes kids can get into some pretty strange stuff at home, and parents have no idea what’s going on until after the fact. That’s basically what happened with this girl who took secret selfies with a horse in her parents’ bathroom. While this hilarious horse selfie scenario is easily one of the weirdest we’ve come across, let’s hear her out. Surely some of the things we did when our parents weren’t home are far more embarrassing (srsly, don’t ask mom and dad).
So, according to Mashable, Twitter user KaitRides decided to have some one-on-one time with her horse Tucker, a 25-year-old, 4.5-feet tall, approximately 1,000-pound steed. Apparently, hanging out in a barn stall sounded like the lamest idea ever because they wound up in front of her parents’ bathroom mirror, which is obviously a suitable space for a big ass horse.
“One time I brought a horse into my parents’ bathroom for mirror selfies and didn’t tell my dad for like 2 years.
One time I brought a horse into my parents' bathroom for mirror selfies and didn't tell my dad for like 2 years.
— KaitRides (@Mares4Life) April 4, 2017
Um, OK. Equine experts and lovers help us out: Is this is a totally normal thing to do when you’re obsessed with horses? Also, how often do horses willingly partake in activities that are typically reserved for humans?
If you need a moment to gather stats and case studies, we’ll wait, but we kind of prefer Kait’s explanation, which sounds totally (not) normal.
“I decided to bring him inside and take pictures with him just because I wanted some really funny selfies that no one had ever seen before,” Kait told Mashable. “I was home from college during a break, and both of my parents were at work for the day, so I knew I could get away with it. He was only in the house for about five minutes, just long enough to go in, take pictures, and leave.”
Huh. So, what’s it like to take a selfie with a horse in a bathroom? Do the usual duckface and light-capturing angle rules still apply?
“Tucker is very laid-back, so it was very easy. I put four hoof boots on him so he wouldn’t get the floor dirty, then I just walked him in,” Kait said.
Wow. Well, congrats on the best horse selfie ever, Kait and Tucker. We’re still confused AF by this entire situation, but also extremely amused.
The comfy, all-year frock has a secret: pockets!
Get great discounts on all kinds of home decor while they're on sale.
Learn to speak, read, and write Thai and Thai script with our easy and fun online course. Nine years of experience teaching individuals and groups.
Even professional musicians are impressed.
An eye pencil for $5, an anti-sagging body lotion at a discount, a vitamin C serum with 68,000 five-star reviews — these are the beauty products Amazon shoppers love.
Abortion restrictions can have harmful economic effects for individual women, state economies and businesses. Research released last year by the Institute for Women’s Policy Research (IWPR) estimated that abortion restrictions may cost the U.S. economy $105 billion per year. Now that Roe v. Wade has been overturned, the cost could be twice has high, the president and CEO of IWPR says.
List Of Healthy Foods You Can Eat Without Gaining Any Weight
Whether it's an image placed on a hospital room door or a term used to describe a baby born after a loss, these symbols and words can support families as they cope with their grief.
Nearly 17,000 five-star fans have already added this tank to their wardrobes.
Take a tour through the scenic Texas Geodome, where guests can relax and enjoy unique architecture and calming nature
You’ll Never Believe What It Grew Into...
Most cases of polio are in children under 5. But being vaccinated protects against the life-threatening disease.
One ice cream maker, 16,000 five-star reviews and countless possibilities.
This gelato tastes like vacation in a bowl
If you aren't wrapping foil around your doorknobs when alone, you should start...
The mom of two has sported many daring looks in recent months.
This traveling couple use their renovated school bus to host pop up photoshoots across the country
Want to enhance your kids’ outdoor playtime? Check out some of the top-rated outdoor toys for 2022.
The dog days of summer are nearly here. Provide your furry friends some relief from the heat with this dog pool.
The question is, which color will you choose?
If those life hacks you've read online aren't working, these cleaning essentials under $10 might help. Get them on Amazon.
Enjoy deep discounts on yard gear at Amazon.
Keep allergy season at bay and save up to 25% off these high-quality air purifiers for your larger rooms.
More than 20,000 five-star fans are already getting good vibes.
You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev.
YOLOv3 is extremely fast and accurate. In mAP measured at .5 IOU YOLOv3 is on par with Focal Loss but about 4x faster. Moreover, you can easily tradeoff between speed and accuracy simply by changing the size of the model, no retraining required!
Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.
We use a totally different approach. We apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.
Our model has several advantages over classifier-based systems. It looks at the whole image at test time so its predictions are informed by global context in the image. It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image. This makes it extremely fast, more than 1000x faster than R-CNN and 100x faster than Fast R-CNN . See our paper for more details on the full system.
YOLOv3 uses a few tricks to improve training and increase performance, including: multi-scale predictions, a better backbone classifier, and more. The full details are in our paper !
This post will guide you through detecting objects with the YOLO system using a pre-trained model. If you don't already have Darknet installed, you should do that first . Or instead of reading all that just run:
You already have the config file for YOLO in the cfg/ subdirectory. You will have to download the pre-trained weight file here (237 MB) . Or just run this:
You will see some output like this:
Darknet prints out the objects it detected, its confidence, and how long it took to find them. We didn't compile Darknet with OpenCV so it can't display the detections directly. Instead, it saves them in predictions.png . You can open it to see the detected objects. Since we are using Darknet on the CPU it takes around 6-12 seconds per image. If we use the GPU version it would be much faster.
I've included some example images to try in case you need inspiration. Try data/eagle.jpg , data/dog.jpg , data/person.jpg , or data/horses.jpg !
The detect command is shorthand for a more general version of the command. It is equivalent to the command:
You don't need to know this if all you want to do is run detection on one image but it's useful to know if you want to do other things like run on a webcam (which you will see later on ).
Instead of supplying an image on the command line, you can leave it blank to try multiple images in a row. Instead you will see a prompt when the config and weights are done loading:
Enter an image path like data/horses.jpg to have it predict boxes for that image.
Once it is done it will prompt you for more paths to try different images. Use Ctrl-C to exit the program once you are done.
By default, YOLO only displays objects detected with a confidence of .25 or higher. You can change this by passing the -thresh flag to the yolo command. For example, to display all detection you can set the threshold to 0:
So that's obviously not super useful but you can set it to different values to control what gets thresholded by the model.
We have a very small model as well for constrained environments, yolov3-tiny . To use this model, first download the weights:
Then run the detector with the tiny config file and weights:
Running YOLO on test data isn't very interesting if you can't see the result. Instead of running it on a bunch of images let's run it on the input from a webcam!
To run this demo you will need to compile Darknet with CUDA and OpenCV . Then run the command:
YOLO will display the current FPS and predicted classes as well as the image with bounding boxes drawn on top of it.
You will need a webcam connected to the computer that OpenCV can connect to or it won't work. If you have multiple webcams connected and want to select which one to use you can pass the flag -c to pick (OpenCV uses webcam 0 by default).
You can also run it on a video file if OpenCV can read the video:
That's how we made the YouTube video above.
You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets. Here's how to get it working on the Pascal VOC dataset.
To train YOLO you will need all of the VOC data from 2007 to 2012. You can find links to the data here . To get all the data, make a directory to store it all and from that directory run:
There will now be a VOCdevkit/ subdirectory with all the VOC training data in it.
Now we need to generate the label files that Darknet uses. Darknet wants a .txt file for each image with a line for each ground truth object in the image that looks like:
Where x , y , width , and height are relative to the image's width and height. To generate these file we will run the voc_label.py script in Darknet's scripts/ directory. Let's just download it again because we are lazy.
After a few minutes, this script will generate all of the requisite files. Mostly it generates a lot of label files in VOCdevkit/VOC2007/labels/ and VOCdevkit/VOC2012/labels/ . In your directory you should see:
The text files like 2007_train.txt list the image files for that year and image set. Darknet needs one text file with all of the images you want to train on. In this example, let's train with everything except the 2007 test set so that we can test our model. Run:
Now we have all the 2007 trainval and the 2012 trainval set in one big list. That's all we have to do for data setup!
Now go to your Darknet directory. We have to change the cfg/voc.data config file to point to your data:
You should replace with the directory where you put the VOC data.
For training we use convolutional weights that are pre-trained on Imagenet. We use weights from the darknet53 model. You can just download the weights for the convolutional layers here (76 MB) .
You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets. Here's how to get it working on the COCO dataset .
To train YOLO you will need all of the COCO data and labels. The script scripts/get_coco_dataset.sh will do this for you. Figure out where you want to put the COCO data and download it, for example:
Now you should have all the data and the labels generated for Darknet.
Now go to your Darknet directory. We have to change the cfg/coco.data config file to point to your data:
You should replace with the directory where you put the COCO data.
You should also modify your model cfg for training instead of testing. cfg/yolo.cfg should look like this:
If you want to use multiple gpus run:
If you want to stop and restart training from a checkpoint:
If you are using YOLO version 2 you can still find the site here: https://pjreddie.com/darknet/yolov2/
If you use YOLOv3 in your work please cite our paper!
Nhận đường liên kết
Facebook
Twitter
Pinterest
Email
Ứng dụng khác
Giới thiệu: Cuộc thi “How-yolo.net – Độ Xõa Của Bạn Thế Nào?” đã trải qua hai phần ba chặng đường, bắt đầu bằng việc kêu gọi các bạn trẻ tham gia gửi ảnh về website how-yolo.net - how-yolo.net để đo độ “xõa” của mình, sau đó chia sẻ trên mạng xã hội với hashtag #yoloselfie. Địa chỉ: 116 Nguyễn Du, Phường Bến Thành, Quận 1, Tp.Hồ Chí Minh SĐT: 0866805564 Hagtag #yoloselfie #lumia540 Website: https://how-yolo.net/ Cuộc thi nhiếp ảnh How yolo.net https://www.kickstarter.com/profile/how-yolonet/about https://www.reddit.com/user/how-yolonet/ https://500px.com/p/howyoloselfie/ https://www.linkedin.com/in/how-yolonet/ https://www.pinterest.com/howyoloselfie/ https://how-yolonet.tumblr.com/ https://about.me/how-yolonet https://www.youtube.com/channel/UCNj4OSeD90UF5saTv3PyKWg/about https://sites.google.com/view/how-yolonet/ https://twitter.com/howyolonet https://issuu.com/how-yolonet
Giới thiệu: Cuộc thi “How-yolo.net – Độ Xõa Của Bạn Thế Nào?” đã trải qua hai phần ba chặng đường, bắt đầu bằng việc kêu gọi các bạn trẻ tham gia gửi ảnh về website how-yolo.net - how-yolo.net để đo độ “xõa” của mình, sau đó chia sẻ trên mạng xã hội với hashtag #yoloselfie.
SĐT: 0866805564
Hagtag #yoloselfie #lumia540
Website: Cuộc thi nhiếp ảnh How yolo.net
https://how-yolo.net/
https://www.kickstarter.com/profile/how-yolonet/about
https://www.reddit.com/user/how-yolonet/
https://500px.com/p/howyoloselfie/
https://about.me/how-yolonet
https://sites.google.com/view/how-yolonet/
https://www.linkedin.com/in/how-yolonet/
https://www.pinterest.com/howyoloselfie/
Pornstars That Love Creampies
Imgur Cum Sluts
Gabrielle Gucci Dp