一个非营利组织的目的是使用人工智能来帮助地球的34,000种鱼类。Wye Foundation的Thomas Wye已建立了鱼类项目,以自动识别鱼类,以帮助教育,new species identification, fish tracking, and fishery management. To do this it has set up a website and then needs people to upload fish images as well as tagging the fish species, and adding the identifying attributes of each fish. Fishial Recognition, backed by a team of biologists and engineers, will then isolate specific traits with the aim of identifying all fish species worldwide.
测试
我们注册了,但是尝试并未导航网站上传图像后必须观看视频。上传后,看起来就像您一样,然后在鱼周围绘制多边形(或为您绘制),然后识别出诸如嘴巴形式,尾巴等的特征,只有在上传了一张Cichlid的图像之后,我们的图像仍在处理,两个小时后。然后,我们尝试了标识工具。这次上传图像就像拖放一样容易,但不幸的是,它猜测了我们错误地上传的每张鱼图片的ID。首先,我们尝试了一个尾皇帝的神干,它被确定为lion鱼。然后,我们尝试了维多利亚湖Cichlid湖,它被认为是罗非鱼。渴望它工作并削减软件的一些懈怠,我们上传了一条暹罗战鱼,贝塔·斯莱斯顿(Betta Splendens),它告诉我们是一条金鱼,随后是无误的普通小丑鱼,这也告诉我们是一条金鱼。我们用Birchir polypterus endlicheri结束了,它被确定为派克。我们在Google镜头上尝试了相同的鱼照片,尽管它没有得到Bichir(也许是方形相框的形状错误?),它首次获得了Ganselfish,Clownfish和Betta。
我们认为
We found Fishial because it was being promoted by an Aquatic business and was asking for help to upload fish photos. We’re not sure if the site we logged into and accessed was an extreme Beta test site, but for us, it was a total failure. Proper species identification is very important to us and before using the site we were wondering how it would cope with species revisions or incredibly similar fish species, both and fresh and saltwater, which have the same outline and morphology, like the Danio genus for example, or Apistogramma. What we found was that the AI was at such an early stage it just didn’t work, and that as of right now Fishial.AI is a million miles away from being credible or usable as a fish reference guide in any aquatic field.
主页和新闻包具有一个大型慈善组织的感觉,但是一旦您超越了我们发现使用它非常令人沮丧,并且不得不在所有内容上观看教程。之后,我们迷失在网站上,如果窗户到处都打开,试图进入两个最重要的部分 - 上传我们自己的鱼照片并从照片中识别鱼,这无效。We are aquarium people, not AI developers, so we’re guessing that Fishial Recognition needs lots of photos uploading to then scan similarities and start making better matches, but the process of uploading photos and information felt like a chore, and something so lengthy that we didn’t want it eating into our very limited spare time.
We’ve beenidentifying fish based on digital photosfor over twenty years and advise that you really need the backing of an organization like Fishbase, which has already cataloged 34900 species, 325600 common names, with 61500 pictures, but really importantly its backed up by another 58900 references, from 2470 collaborators. And if fish ID gets super difficult, Ichthyologists live in the digital space too and you can even ask the world expert on deepwater gobies for example what they think it is. As long as you have a signal, you can get a fish species ID from anywhere in the world.
We promise to revisit Fishial.AI with its quirkily named Fishial Recognition. We want it to work and if it does it will become a very valuable resource. Google does reign supreme however and we are used to the instant nature of uploading photos to social media. We are sure there are some brilliant minds behind the project with tons of hope and ambition, but based on our short user experience versus just about every other resource online, it will take some epic artificial intelligence to catch up.