2e:null 2f:["$","title",null,{"children":"Paintings of Butterfly: Making Of | Zihou Ng | Observable"}] 34:Tbaf,The neural network is a simple feed forward one with some residual blocks ``` ============================================================================ Layer (type:depth-idx) Output Shape Param # ============================================================================ RotationNetwork -- -- ├─Sequential: 1-1 [1, 2] -- │ └─Conv2d: 2-1 [1, 8, 64, 64] 80 │ └─BatchNorm2d: 2-2 [1, 8, 64, 64] 16 │ └─ReLU: 2-3 [1, 8, 64, 64] -- │ └─EncoderBlock: 2-4 [1, 8, 32, 32] -- │ │ └─ResidualBlock: 3-1 [1, 8, 64, 64] 1,200 │ │ └─ResidualBlock: 3-2 [1, 8, 64, 64] 1,200 │ │ └─Sequential: 3-3 [1, 8, 32, 32] 1,048 │ └─EncoderBlock: 2-5 [1, 16, 16, 16] -- │ │ └─ResidualBlock: 3-4 [1, 8, 32, 32] 1,200 │ │ └─ResidualBlock: 3-5 [1, 8, 32, 32] 1,200 │ │ └─Sequential: 3-6 [1, 16, 16, 16] 2,096 │ └─EncoderBlock: 2-6 [1, 32, 8, 8] -- │ │ └─ResidualBlock: 3-7 [1, 16, 16, 16] 4,704 │ │ └─ResidualBlock: 3-8 [1, 16, 16, 16] 4,704 │ │ └─Sequential: 3-9 [1, 32, 8, 8] 8,288 │ └─EncoderBlock: 2-7 [1, 64, 4, 4] -- │ │ └─ResidualBlock: 3-10 [1, 32, 8, 8] 18,624 │ │ └─ResidualBlock: 3-11 [1, 32, 8, 8] 18,624 │ │ └─Sequential: 3-12 [1, 64, 4, 4] 32,960 │ └─EncoderBlock: 2-8 [1, 64, 1, 1] -- │ │ └─ResidualBlock: 3-13 [1, 64, 4, 4] 74,112 │ │ └─ResidualBlock: 3-14 [1, 64, 4, 4] 74,112 │ │ └─Sequential: 3-15 [1, 64, 1, 1] 65,728 │ └─Flatten: 2-9 [1, 64] -- │ └─Linear: 2-10 [1, 64] 4,160 │ └─BatchNorm1d: 2-11 [1, 64] 128 │ └─ReLU: 2-12 [1, 64] -- │ └─Linear: 2-13 [1, 2] 130 │ └─Tanh: 2-14 [1, 2] -- ============================================================================ Total params: 314,314 Trainable params: 314,314 Non-trainable params: 0 Total mult-adds (M): 50.64 ``` The training took less than 10 mins and the results were quite 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32:{"id":"621bbe5a95e36d9d","slug":"paintings-of-butterfly-making-of","title":"Paintings of Butterfly: Making Of","thumbnail":"2cbe686d87c0917fd8de4b6a9dfea3928349afd1bfbd9a73ce2c3ab3781d65f0","access_level":"public","create_time":"$D2021-05-17T12:23:07.002Z","update_time":"$D2021-05-22T01:26:11.393Z","view_time":null,"first_public_time":"$D2021-05-20T05:57:30.093Z","has_unpublished_edits":null,"fork":false,"stars":9,"starred":false,"owner":{"type":"team","id":"7fad80bf128915e9","login":"stwind","name":"Zihou Ng","avatar_url":"https://avatars-next.observableusercontent.com/ff8a8f73ad220dcaec180c1e813951d692777837355004f062692b49042d2458","tier":"starter_2024"},"version":304,"publish_version":304,"role":null,"authored":false,"authors":[{"type":"individual","id":"dbb85afd665c9e5e","login":"stwind","name":"Zihou Ng","avatar_url":"https://avatars-next.observableusercontent.com/ff8a8f73ad220dcaec180c1e813951d692777837355004f062692b49042d2458","tier":null}],"members":[{"type":"individual","id":"dbb85afd665c9e5e","login":"stwind","name":"Zihou Ng","avatar_url":"https://avatars-next.observableusercontent.com/ff8a8f73ad220dcaec180c1e813951d692777837355004f062692b49042d2458","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Paintings of Butterfly: Making Of","cells":[{"id":0,"value":"# Paintings of Butterfly: Making Of","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"[Paintings of Butterfly](https://observablehq.com/@stwind/paintings-of-butterfly) is an experiment of generating butterfly drawings with StyleGAN2, in an attempt to mimic what scientists was doing with [CRISPR](https://entomologytoday.org/2020/01/09/crispr-cuts-through-layers-butterfly-wing-pattern-evolution/). To me there is always a certain sense of similarity between DNA manipulation and StyleGAN2 latent space navigation.\n\nThis is a documentation of the making process.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"## Data Collection\n\nThe [Biodiversity Heritage Library](https://www.biodiversitylibrary.org/) has a large collection of legacy literature of biodiversity, and they are available for open access. That's a good starting point. \n\nThere are about 6.5k photo albums on their [Flickr page](https://www.flickr.com/photos/biodivlibrary/albums/). I spent some hours to skim over the album covers, and picked out 200+ of them that I believed containing lots of butterfly drawings.\n\nThen I downloaded the albums and did a quick check of the thumbnails to throw out those obviously having no butterflies in it.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"html`

Die Gross-Schmetterlinge der Erde : eine systematische Bearbeitung der bis jetzt bekannten Gross-Schmetterlinge (1906)

\n\n

Nomenclatur und Beschreibung der Insecten in der Grafschaft Hanau-Münzenberg wie auch der Wetterau und der angränzenden Nachbarschaft dies und jenseits des Mains mit erleuchteten Kupfern

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"## Annotating for Objects Detection\n\nIn order to extract the butterflies in the pictures, I randomly picked out few hundreds of them and used [CVAT](https://github.com/openvinotoolkit/cvat) to annotate the regions of butterflies.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"html`

\n\n

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"Then I exported the annotations as [COCO](https://cocodataset.org/) format.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"html`

\n\n

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"## Detecting Butterflies\n\nWith the annotation ready, I used [detectron2](https://github.com/facebookresearch/detectron2) to train an [Fast RCNN](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml) to find all the butterflies in the pictures. It took about an hour and the results are satisfying.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"html`

\n\n

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"Here are the best and worst cases, even the worst ones are good enough to contain a single butterfly.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"html`

\n\n

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"I kept the results with a score > 0.9 and with area larger than 256x256, then cropped out a square region for each of them.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"## Data Alignment\n\nThe data are not ready for StyleGAN2 yet, to get better results, they needed to be aligned: centering and rotation.\n\n### Finding Countours with U^2 Net\n\nFor centering, the idea was to find the contour of a butterfly with [U^2-Net](https://github.com/xuebinqin/U-2-Net) to do a salicency detection, and used the resulting mask to find the centroid.\n\nI picked out 1000 of them and used OpenCV to find a rough contour like this:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"Then I used [Pixelmator](https://www.pixelmator.com/pro/) to do some manual refinements. Thanks to the [Applescript Support](https://www.pixelmator.com/tutorials/resources/advanced-automation-and-scripting-with-applescript/) of Pixelmator, this process can be largely automated. Masking hundreds of images took another few hours.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":165,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"Now we are ready for the U^2-Net training. The model was trained for 30 epochs to reach an [IOU](https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) of 0.96","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":177,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"Here are the best and worst cases. It is not perfect but good enough since we are only interested in finding centroids.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"html`

\n\n

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"Here is a subset of the centered masks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"## Rotational Alignment\n\nTo rotationaly align the images, we can train an neural network that outputs the rotated angles given the mask images.\n\nAgain I picked some of the masks, and built a simplistic custom labeling tool on Observable, to label rotation angles.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":205,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":208,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"Here is a subset of the rotated masks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":225,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"With the centroids and rotation angles, I can finally compile an aligned dataset of butterflies for StyleGAN2. There are still some inevitable bad cases but they are generally negligible.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"html`

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":246,"value":"## Training StyleGAN2\n\nWe are finally reach the fun part. I trained a StyleGAN2 using [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada-pytorch), transferred from FFHQ512, on a Tesla V100 GPU for 22 hours (1000 kimg). Thanks to all the optimizations of NVlabs' implementation, this process was quite straight forward.\n\nHere are some quick result checks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"html`

\n\n

\n\n

`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":283,"value":"For more selected results, see [Paintings Of Butterfly](https://observablehq.com/@stwind/paintings-of-butterfly).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 33:"$32:body"