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Training Poses
lav.ioSam Lavigne Work / Teaching / About ❬ Training Pose
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SEO audit: Content analysis
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| Title | Training Poses | ||||||||||||||||||||||||||||||||||||
| Text / HTML ratio | 56 % | ||||||||||||||||||||||||||||||||||||
| Frame | Excellent! The website does not use iFrame solutions. | ||||||||||||||||||||||||||||||||||||
| Flash | Excellent! The website does not have any flash contents. | ||||||||||||||||||||||||||||||||||||
| Keywords cloud | images COCO random match original OpenPose system pose objects natural context” common scenes estimate everyday human poses attempt complex “gathering | ||||||||||||||||||||||||||||||||||||
| Keywords consistency |
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| Images | We found 4 images on this web page. |
SEO Keywords (Single)
| Keyword | Occurrence | Density |
|---|---|---|
| images | 4 | 0.20 % |
| COCO | 3 | 0.15 % |
| random | 2 | 0.10 % |
| match | 2 | 0.10 % |
| original | 2 | 0.10 % |
| OpenPose | 2 | 0.10 % |
| system | 2 | 0.10 % |
| pose | 2 | 0.10 % |
| objects | 1 | 0.05 % |
| natural | 1 | 0.05 % |
| context” | 1 | 0.05 % |
| common | 1 | 0.05 % |
| scenes | 1 | 0.05 % |
| estimate | 1 | 0.05 % |
| everyday | 1 | 0.05 % |
| human | 1 | 0.05 % |
| poses | 1 | 0.05 % |
| attempt | 1 | 0.05 % |
| complex | 1 | 0.05 % |
| “gathering | 1 | 0.05 % |
SEO Keywords (Two Word)
| Keyword | Occurrence | Density |
|---|---|---|
| and then | 2 | 0.10 % |
| of the | 2 | 0.10 % |
| the original | 2 | 0.10 % |
| which is | 2 | 0.10 % |
| to match | 2 | 0.10 % |
| a random | 2 | 0.10 % |
| images on | 1 | 0.05 % |
| selection of | 1 | 0.05 % |
| original COCO | 1 | 0.05 % |
| COCO images | 1 | 0.05 % |
| Sam Lavigne | 1 | 0.05 % |
| on which | 1 | 0.05 % |
| which the | 1 | 0.05 % |
| the system | 1 | 0.05 % |
| system was | 1 | 0.05 % |
| was trained | 1 | 0.05 % |
| trained A | 1 | 0.05 % |
| A python | 1 | 0.05 % |
| random selection | 1 | 0.05 % |
| with a | 1 | 0.05 % |
SEO Keywords (Three Word)
| Keyword | Occurrence | Density | Possible Spam |
|---|---|---|---|
| Sam Lavigne Work | 1 | 0.05 % | No |
| which the system | 1 | 0.05 % | No |
| of the original | 1 | 0.05 % | No |
| the original COCO | 1 | 0.05 % | No |
| original COCO images | 1 | 0.05 % | No |
| COCO images on | 1 | 0.05 % | No |
| images on which | 1 | 0.05 % | No |
| on which the | 1 | 0.05 % | No |
| the system was | 1 | 0.05 % | No |
| random selection of | 1 | 0.05 % | No |
| system was trained | 1 | 0.05 % | No |
| was trained A | 1 | 0.05 % | No |
| trained A python | 1 | 0.05 % | No |
| A python script | 1 | 0.05 % | No |
| python script chooses | 1 | 0.05 % | No |
| script chooses a | 1 | 0.05 % | No |
| chooses a random | 1 | 0.05 % | No |
| selection of the | 1 | 0.05 % | No |
| a random selection | 1 | 0.05 % | No |
| random COCO image | 1 | 0.05 % | No |
SEO Keywords (Four Word)
| Keyword | Occurrence | Density | Possible Spam |
|---|---|---|---|
| Sam Lavigne Work Teaching | 1 | 0.05 % | No |
| which the system was | 1 | 0.05 % | No |
| of the original COCO | 1 | 0.05 % | No |
| the original COCO images | 1 | 0.05 % | No |
| original COCO images on | 1 | 0.05 % | No |
| COCO images on which | 1 | 0.05 % | No |
| images on which the | 1 | 0.05 % | No |
| on which the system | 1 | 0.05 % | No |
| the system was trained | 1 | 0.05 % | No |
| random selection of the | 1 | 0.05 % | No |
| system was trained A | 1 | 0.05 % | No |
| was trained A python | 1 | 0.05 % | No |
| trained A python script | 1 | 0.05 % | No |
| A python script chooses | 1 | 0.05 % | No |
| python script chooses a | 1 | 0.05 % | No |
| script chooses a random | 1 | 0.05 % | No |
| chooses a random COCO | 1 | 0.05 % | No |
| selection of the original | 1 | 0.05 % | No |
| a random selection of | 1 | 0.05 % | No |
| random COCO image and | 1 | 0.05 % | No |
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Training Poses Sam Lavigne Work / Teaching / About ❬ Training Poses 2018 · online, print http://lav.io/training/ ❭ Microsoft’s COCO (Common Objects in Context) dataset contains 220,000 labeled images, originally sourced from Flickr, and then tagged and annotated by workers on Amazon Mechanical Turk. The goal of the project, which is used as training material for myriad machine learning systems, is to enable state-of-the-art object recognition by “gathering images of ramified everyday scenes containing worldwide objects in their natural context.” One such system is OpenPose, which is worldly-wise to estimate human poses from still images. Here I struggle to match my pose with a random selection of the original COCO images on which the system was trained. A python script chooses a random COCO image and subject for me to imitate, and I try to match its pose, using OpenPose to track myself in real time. When I succeed (within a threshold) a photo is automatically taken and then superimposed on the original.