<story><title>Everyone wants to do the model work, not the data work [pdf]</title><url>https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf</url></story><parent_chain><item><author>sillysaurusx</author><text>Little crafty trick: grab the flickr dataset (yfcc100m) and snag photos tagged as whatever you want. It&amp;#x27;s like a janky google image search. I&amp;#x27;ve put together datasets of airplanes, bicycles, dogs, etc this way.&lt;p&gt;It&amp;#x27;s not entirely accurate, but it&amp;#x27;s good enough. Within a few hours you can have a pretty large dataset of whatever you want, really. (Yay for massive dataset plus user tags.)&lt;p&gt;You can snag a copy from my server, if you want. (Warning: It&amp;#x27;s a direct link to a 54GB json file.) &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;battle.shawwn.com&amp;#x2F;sdc&amp;#x2F;f100m&amp;#x2F;yfcc100m_dataset.json&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;battle.shawwn.com&amp;#x2F;sdc&amp;#x2F;f100m&amp;#x2F;yfcc100m_dataset.json&lt;/a&gt;&lt;p&gt;I have a script called &amp;quot;janky-image-search&amp;quot; that returns random results from searching this dataset. Here are a few random hits for `janky-image-search ceiling`:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;35981213@N00&amp;#x2F;9374577304&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;35981213@N00&amp;#x2F;9374577304&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;farm3.staticflickr.com&amp;#x2F;2084&amp;#x2F;2513539756_7a768de44c.jpg&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;farm3.staticflickr.com&amp;#x2F;2084&amp;#x2F;2513539756_7a768de44c.jp...&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;96453841@N00&amp;#x2F;7077625757&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;96453841@N00&amp;#x2F;7077625757&amp;#x2F;&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;28742299@N04&amp;#x2F;4683245369&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;28742299@N04&amp;#x2F;4683245369&amp;#x2F;&lt;/a&gt;&lt;p&gt;etc.&lt;p&gt;The quality is hit or miss, but it seems better than 70%.&lt;p&gt;EDIT: Here&amp;#x27;s a gallery of the first 100 hits for &amp;quot;ceiling&amp;quot;: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;cdn.gather.town&amp;#x2F;storage.googleapis.com&amp;#x2F;gather-town.appspot.com&amp;#x2F;uploads&amp;#x2F;&amp;#x2F;8COCgBeMAjI2etwdmTEJSB?:id=ceiling.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;cdn.gather.town&amp;#x2F;storage.googleapis.com&amp;#x2F;gather-town.a...&lt;/a&gt;&lt;p&gt;But as you can see, it&amp;#x27;s not effortless. Most of those ceilings are old. So it depends what you want. It&amp;#x27;s why labeled data is worth billions of dollars (scale.ai et al).</text></item><item><author>sanxiyn</author><text>Real story. I worked on nudity detection for mobile realtime video chat app with on-device deep learning model. (It was successful.) Should be easy: label some data, start from a good network pretrained on ImageNet (we used SqueezeNet), fine tune. The problem was that ImageNet is photos, not video frames, and distribution of video frames from mobile video is somewhat different. Incredibly large proportion of frames are either blank (camera is blocked) or ceiling (camera is misdirected). We ended up adding lots of data for blank and ceiling explicitly labeled as blank and ceiling. It became an excellent ceiling detector, detecting ceilings it never have seen before.&lt;p&gt;Is there a mobile video frames dataset for labeling ceilings? Why not? I can&amp;#x27;t believe I am the only one who experienced this. Why is not a ceiling dataset worthy of, say, CVPR? It will improve mobile video on-device deep learning more than most of CVPR papers. This is a serious problem.&lt;p&gt;Edit: I understand why niche datasets remain in industry and not academia. Public datasets are better if they are of general interest. But ceiling dataset is of general interest to anyone who wants to process video frames originating from blocked or misdirected camera aka smartphones, and it&amp;#x27;s hard to imagine topics of more general interest once you have obvious things like face.</text></item></parent_chain><comment><author>willvarfar</author><text>Super neat!&lt;p&gt;(Small detail: the first 100 hits for ceilings are all _interesting_ ceilings?  Nothing like the kind of ceilings usually encountered on a video chat app?)</text></comment>
<story><title>Everyone wants to do the model work, not the data work [pdf]</title><url>https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf</url></story><parent_chain><item><author>sillysaurusx</author><text>Little crafty trick: grab the flickr dataset (yfcc100m) and snag photos tagged as whatever you want. It&amp;#x27;s like a janky google image search. I&amp;#x27;ve put together datasets of airplanes, bicycles, dogs, etc this way.&lt;p&gt;It&amp;#x27;s not entirely accurate, but it&amp;#x27;s good enough. Within a few hours you can have a pretty large dataset of whatever you want, really. (Yay for massive dataset plus user tags.)&lt;p&gt;You can snag a copy from my server, if you want. (Warning: It&amp;#x27;s a direct link to a 54GB json file.) &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;battle.shawwn.com&amp;#x2F;sdc&amp;#x2F;f100m&amp;#x2F;yfcc100m_dataset.json&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;battle.shawwn.com&amp;#x2F;sdc&amp;#x2F;f100m&amp;#x2F;yfcc100m_dataset.json&lt;/a&gt;&lt;p&gt;I have a script called &amp;quot;janky-image-search&amp;quot; that returns random results from searching this dataset. Here are a few random hits for `janky-image-search ceiling`:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;35981213@N00&amp;#x2F;9374577304&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;35981213@N00&amp;#x2F;9374577304&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;farm3.staticflickr.com&amp;#x2F;2084&amp;#x2F;2513539756_7a768de44c.jpg&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;farm3.staticflickr.com&amp;#x2F;2084&amp;#x2F;2513539756_7a768de44c.jp...&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;96453841@N00&amp;#x2F;7077625757&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;96453841@N00&amp;#x2F;7077625757&amp;#x2F;&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;28742299@N04&amp;#x2F;4683245369&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.flickr.com&amp;#x2F;photos&amp;#x2F;28742299@N04&amp;#x2F;4683245369&amp;#x2F;&lt;/a&gt;&lt;p&gt;etc.&lt;p&gt;The quality is hit or miss, but it seems better than 70%.&lt;p&gt;EDIT: Here&amp;#x27;s a gallery of the first 100 hits for &amp;quot;ceiling&amp;quot;: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;cdn.gather.town&amp;#x2F;storage.googleapis.com&amp;#x2F;gather-town.appspot.com&amp;#x2F;uploads&amp;#x2F;&amp;#x2F;8COCgBeMAjI2etwdmTEJSB?:id=ceiling.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;cdn.gather.town&amp;#x2F;storage.googleapis.com&amp;#x2F;gather-town.a...&lt;/a&gt;&lt;p&gt;But as you can see, it&amp;#x27;s not effortless. Most of those ceilings are old. So it depends what you want. It&amp;#x27;s why labeled data is worth billions of dollars (scale.ai et al).</text></item><item><author>sanxiyn</author><text>Real story. I worked on nudity detection for mobile realtime video chat app with on-device deep learning model. (It was successful.) Should be easy: label some data, start from a good network pretrained on ImageNet (we used SqueezeNet), fine tune. The problem was that ImageNet is photos, not video frames, and distribution of video frames from mobile video is somewhat different. Incredibly large proportion of frames are either blank (camera is blocked) or ceiling (camera is misdirected). We ended up adding lots of data for blank and ceiling explicitly labeled as blank and ceiling. It became an excellent ceiling detector, detecting ceilings it never have seen before.&lt;p&gt;Is there a mobile video frames dataset for labeling ceilings? Why not? I can&amp;#x27;t believe I am the only one who experienced this. Why is not a ceiling dataset worthy of, say, CVPR? It will improve mobile video on-device deep learning more than most of CVPR papers. This is a serious problem.&lt;p&gt;Edit: I understand why niche datasets remain in industry and not academia. Public datasets are better if they are of general interest. But ceiling dataset is of general interest to anyone who wants to process video frames originating from blocked or misdirected camera aka smartphones, and it&amp;#x27;s hard to imagine topics of more general interest once you have obvious things like face.</text></item></parent_chain><comment><author>sanxiyn</author><text>I found that people don&amp;#x27;t upload misdirected ceiling photos and videos to YouTube and Flickr. How unfortunate. Yes, it&amp;#x27;s better than nothing.</text></comment>