The current logo detection service expects image URLs from the upload stash, e.g., https://commons.wikimedia.org/wiki/Special:UploadStash/file/1avpfxdmdb4c.deuia.10893556.png.
Passing image objects would certainly improve the service performance.
See https://gitlab.wikimedia.org/mfossati/scriptz/-/merge_requests/8#note_75813 for more details.
Description
Details
Subject | Repo | Branch | Lines +/- | |
---|---|---|---|---|
logo-detection: process image objects instead of image URLs | machinelearning/liftwing/inference-services | main | +58 -25 |
Status | Subtype | Assigned | Task | ||
---|---|---|---|---|---|
Open | None | T349641 [EPIC] MVP Logo machine detection on Commons | |||
Resolved | kevinbazira | T358676 Host a logo detection model for Commons images | |||
Resolved | kevinbazira | T363506 Pass image objects to the logo detection service |
Event Timeline
@mfossati I am in favor of passing the image object in some serialized form.
We would need the upload wizard to send a resized image (224x224) instead of the whole file. Is that something you are already considering or think it would be easy to try?
We haven't thought of this yet, mainly because pre-processing logic on the model side already handles resizing. That said, I agree it'd be better to directly send the 224x224 image object.
We haven't thought of this yet, mainly because pre-processing logic on the model side already handles resizing. That said, I agree it'd be better to directly send the 224x224 image object.
Preprocessing will just make sure that the images passed to the model for inference are of the right size, which means that if we pass a 224x224 image it will basically do nothing to it, which is fine.
The main reason to do this would be to guarantee specific latencies especially in the case of larger images as we wouldn't have to download the files (which causes unpredictable response times from the model server).
If you agree we can look into the upload stash code and come up with a proposal.
cc: @kevinbazira
As discussed in today's meeting, adding image objects to the API request significantly increases the payload size. See sample payloads in P62085. If one user sends a request with 50 image URLs and another sends a request with 50 serialized images objects, the latter is likely to exceed the server's request body size limit faster.
@mfossati We noticed that the user can define the width in the url like in this example http://commons.wikimedia.org/w/index.php?title=Special:FilePath&file=Cambia_logo.png&width=224. If we can use this then it would be sufficient and we can stick with using urls in the request.
In this case we can change the request to just include the image name and we can construct the remaining url. Do you know if the name is the unique identifier for the image?
An request then would look like this
{ "instances": [ { "filename": "Cambia_logo.png", "target": "logo" } ] }
Hmm, I've just given it a try and I think it won't work for stashed images, which is a hard requirement for us.
Regarding image sizes, at the moment Wikimedia Commons cannot serve a file larger than 1MB from the UploadStash. I am getting the following error:
To reproduce this, please follow the steps below:
- visit the Commons UploadWizard: https://commons.wikimedia.org/wiki/Special:UploadWizard
- use the UploadWizard to upload an image that is above 2MBs but don't publish it
- visit the Commons UploadStash: https://commons.wikimedia.org/wiki/Special:UploadStash
- copy URL of stash key and try to access it in a new tab. you'll get an internal server error: Cannot serve a file larger than 1048576 bytes.
Thinking out loud: what about sending multiple requests if the limit is reached? I speculate that 50 uploads are an edge case: if this happens, we could dispatch different requests.
More context: we're planning to plug the logo detection inside the Upload Wizard workflow, so I think that the actual LiftWing service user will be some client-side logic that lives in Upload Wizard's codebase.
I can imagine we can tackle that from within the Upload Wizard with some JavaScript library. I can create a ticket to look into that if you think this would be the best solution.
@isarantopoulos @kevinbazira , I think I found how to get a thumbnail from a stashed image. There you go: https://commons.wikimedia.org/wiki/Special:UploadStash/thumb/1awuam969hko.2tkfbz.10893556.png/224px-1awuam969hko.2tkfbz.10893556.png, where 1awuam969hko.2tkfbz.10893556.png is the stash file key. The 224px- prefix is the width size.
Of course, I feel there's a caveat, as it seems that the thumbnail is generated on the fly at request time. Still not optimal, but sounds like a workable solution.
I've opened T364551: [SPIKE] Send an image thumbnail to the logo detection service to investigate the feasibility of this solution.
@isarantopoulos @kevinbazira, I'd like to set expectations right here: my team is aiming at a workable solution in roughly one month. Optimization can then come as a subsequent step. I think that T363506#9783689 is fair enough for now. Do you agree?
CC our product manager @AUgolnikova-WMF .
Thank you for dedicating a task to investigate the feasibility of this solution.
I'd like to set expectations right here: my team is aiming at a workable solution in roughly one month. Optimization can then come as a subsequent step. I think that T363506#9783689 is fair enough for now. Do you agree?
100% agree, I am able to reproduce the 224px- thumbnail solution on my end and the inference results are not far apart:
# request without thumbnail $ time curl -s localhost:8080/v1/models/logo-detection:predict -X POST -d '{"instances": [ { "filename": "woman-with-dog--on-a-beach.jpeg", "url": "https://commons.wikimedia.org/wiki/Special:UploadStash/file/1aww29uyg1ik.hl80le.7972071.jpg", "target": "logo" } ] }' -i -H "Content-type: application/json" -H "Cookie: <redacted>" HTTP/1.1 200 OK date: Thu, 09 May 2024 16:28:54 GMT server: uvicorn content-length: 121 content-type: application/json {"predictions":[{"filename":"woman-with-dog--on-a-beach.jpeg","target":"logo","prediction":0.015,"out_of_domain":0.985}]} real 0m0.730s user 0m0.000s sys 0m0.022s # request with thumbnail $ time curl -s localhost:8080/v1/models/logo-detection:predict -X POST -d '{"instances": [ { "filename": "woman-with-dog--on-a-beach.jpeg", "url": "https://commons.wikimedia.org/wiki/Special:UploadStash/thumb/1aww29uyg1ik.hl80le.7972071.jpg/120px-1aww29uyg1ik.hl80le.7972071.jpg", "target": "logo" } ] }' -i -H "Content-type: application/json" -H "Cookie: <redacted>" HTTP/1.1 200 OK date: Thu, 09 May 2024 16:28:28 GMT server: uvicorn content-length: 123 content-type: application/json {"predictions":[{"filename":"woman-with-dog--on-a-beach.jpeg","target":"logo","prediction":0.0206,"out_of_domain":0.9794}]} real 0m0.824s user 0m0.012s sys 0m0.012s
During the meeting between the Structured Content team and ML team, it was concluded that passing image objects is preferable to passing image URLs. This is because passing image URLs raises security concerns related to sharing a user cookie, as discussed in T362749.
@mfossati, currently the logo-detection model-server takes the following input with an image URL:
{ "instances": [ { "filename": "Cambia_logo.png", "url": "http://commons.wikimedia.org/w/index.php?title=Special:FilePath&file=Cambia_logo.png&width=224", "target": "logo" } ] }
To facilitate the transition to image objects, please specify:
- what the new input will look like
- what serialization format will be used
Thanks!
We concluded that we will figure out the format after the team figures out the spike (accessing the image and sending a thumbnail to Lift Wing).
I'd suggest we proceed with a base64 encoded image for now. Something like this would work:
{ "instances": [ { "image": BASE_64_ENCODED_STRING "target": "logo" } ] }
In this context I would recommend we don't rewrite the whole app. We can add a new function loading the image in memory and use that one instead of fetching the urls.
We also said that it would be great to include the content-length header in the request so that we will know what to expect.
See T364551: [SPIKE] Send an image thumbnail to the logo detection service
I'd suggest we proceed with a base64 encoded image for now.
With binary being the preferred format, right?
We also said that it would be great to include the content-length header in the request so that we will know what to expect.
Sure, noted in the spike.
The logo-detection model-server has been updated to process base64 image objects instead of image URLs. Below is what the new input and output look like. We tested it with the same images used during the prototype validation, and the output results are not far apart, as shown in P58917#237882.
1.Request with sample payload that has 6 base64 image objects:
curl -s localhost:8080/v1/models/logo-detection:predict -X POST -d '{"instances": [{"filename": "12_rue_de_Condé_-_detail.jpg", "image": "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"target": "logo"},{"filename": "120px-Abv.png", "image": 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", 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", "target": "logo"},{"filename": "Elizabeth_Drive_-_border_of_Edensor_Park_and_Bonnyrigg_Heights_in_New_South_Wales_62.jpg", "image": "/9j/4gIoSUNDX1BST0ZJTEUAAQEAAAIYYXBwbAQAAABtbnRyUkdCIFhZWiAH5gABAAEAAAAAAABhY3NwQVBQTAAAAABBUFBMAAAAAAAAAAAAAAAAAAAAAAAA9tYAAQAAAADTLWFwcGzs/aOOOIVHw220vU962hgvAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAApkZXNjAAAA/AAAADBjcHJ0AAABLAAAAFB3dHB0AAABfAAAABRyWFlaAAABkAAAABRnWFlaAAABpAAAABRiWFlaAAABuAAAABRyVFJDAAABzAAAACBjaGFkAAAB7AAAACxiVFJDAAABzAAAACBnVFJDAAABzAAAACBtbHVjAAAAAAAAAAEAAAAMZW5VUwAAABQAAAAcAEQAaQBzAHAAbABhAHkAIABQADNtbHVjAAAAAAAAAAEAAAAMZW5VUwAAADQAAAAcAEMAbwBwAHkAcgBpAGcAaAB0ACAAQQBwAHAAbABlACAASQBuAGMALgAsACAAMgAwADIAMlhZWiAAAAAAAAD21QABAAAAANMsWFlaIAAAAAAAAIPfAAA9v////7tYWVogAAAAAAAASr8AALE3AAAKuVhZWiAAAAAAAAAoOAAAEQsAAMi5cGFyYQAAAAAAAwAAAAJmZgAA8qcAAA1ZAAAT0AAACltzZjMyAAAAAAABDEIAAAXe///zJgAAB5MAAP2Q///7ov///aMAAAPcAADAbv/bAEMABAMDBAMDBAQDBAUEBAUGCgcGBgYGDQkKCAoPDRAQDw0PDhETGBQREhcSDg8VHBUXGRkbGxsQFB0fHRofGBobGv/bAEMBBAUFBgUGDAcHDBoRDxEaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGv/AABEIAFoAeAMBIgACEQEDEQH/xAAdAAACAgMBAQEAAAAAAAAAAAAFBgQHAAIDCAEJ/8QAPBAAAgEDBAEDAgMECAUFAAAAAQIDBAURAAYSITETIkEHURQjYQgyQnEVM0NigZGhsRYXUnLwU5LB0fH/xAAaAQACAwEBAAAAAAAAAAAAAAABAgADBAUG/8QAJxEAAgICAgIBAwUBAAAAAAAAAAECEQMhEjEEQRNRgaEiMpGx8MH/2gAMAwEAAhEDEQA/AE6oml2BWVdRs+riqqEqWqrVM4/KbyHix4HkBTg9fOgO4971e9bK0FfHFBJHP66RRU7M2MH3E5zjB/20pWPdVfZql5SI6uOZGjlE0YYup8jJ1tbrrRU06SRRVNPKXHOQS54Lnvjxx/rr0i8ZrbVtezhfKnpdGlC0s1MHimYkMQq4J5KB22f8h/8Amu/osQQU9+R5+NWxsq3bO3bE5rppLTVE4iibigK/fmT2xOTk4860u+wKWKWolt0zvT06h5DImCAWwD9iCPBHnXOzTubVUascf03ZX9DSLERNOUQY+3+mmGO6f0fKrwjoL0MeNdK7akktuqJKGRucbZMZ6DAeSM6UfxUiH05ZQyr17h3rN2XLQTuV6qLhKz1UzsD4DHrXP8TKZDIkjo4UKCrEdaipTmqClFBbPx4Om/bG1/x9Usc4wzn+I4XU5ULQKsdllulVFFGrYznodZ1ZdJ9NpY8LGv5jKWORjA1buz9gW+y01BV3b8BboXHpxtJUZM74ODj48eB9tE9wX/aNHuaOhrqirWYQpKUFGVjMbAe95M5wQCw68frqnnKTpIbUVspGXYs1veMS0bYbsMyYB0Yo9szBeXpKnEZHtwB35z9tXrXXTbVPZHKzRVaSDjDFz5MCwHv4+VUAhmJwBkaTaq/LbIaShs0sVwpB+8DAGBZs82Vs5AxxwD5x8aVNy9DWkJ+3tq1VTVsIY/VmfwUX4Oe9P8f0yV4FW7V70tuGWkbpAxxjHZGe9bba2491qiTSTwwDDGX1OKxqPnHjGfA0Q3DuygtdJNbLHRT10hjaD8XVBgsUg6yEcEMQCf5dalSlKogbilbFuru22Nl1T0lkpluFfEhVqkBZoadsHHFelkfP8RyBrNKclIGBD9lR58ADzk6zXQh48a6sqeUrSp+nmydwUsD0lXJYrpUSt6sk0sjI2TnK+VJz1ggef01X932Fcto3yWivVFM8UcXr+qsPNfRzkSBh7W/kD3ovtT6pbWrab16i5/0BxLZjuNPkMo7BDKGBJH8Pn7Z1etloaHf23aOrWotm6LJMjJSfhZEQt5EgMb4bmAD0ACPPjSYfNnDTdoE8EXtKmURGLfIIVsxp+DjlxiYkjrGMEkjxnH66tmm3gli2G9A5p6iuqGWONajL+ggOeQXHbEDiO8Lk6Cb92G2w2/GbZpkNqq4xRsXlLyUrhjJwdD2jYHTZYN2evGhNt3DVzW1aSVFmRmHJW4szAfGfI0MrjLceiY7Spmu6NxNc7XQQN7ain9RRIAq/lEjCNjtiDn3Hv40jw0qyviVORJ+NO9/tW2Zrc1TUC40twKYhWnZSmQe+QI84yOjpesdpSSdHi/ETxhsMrLg+fuM6q1Wiw60dsjQKiHJJ7x3q0dkWyklrljq5PwyLEz9uuchTge4/J+NNmwtlW++y0ENft+ICacKZFqJl4xj3O+P3eh0QWB7BAOg9Rv2KK9x0H092paZS9UaeKSpp5JGkPqERoGZwAxUAk9eT9tLGMsjaj6BKSgtlg7Qt0IF0u13tsvCjRTSRrIyxszYVVBJ6Zjjxqod2TXrdO+plutDOtzn4RwW9ISWRADwjRPOAAf8AIn76umC37urLhbLvX3eGraCvj9a2LB6FNRxcGyygHMvYUe7GOIP3zN3DSt/w3Kds2t7hUPBKq13fFDIWI93LkVXkSBnAPjrQx5Fid9/8JKHNV0JuzvpstHMKbc609PJWOiNSJOjyqiEtlypIVWPXn4Oe8DTPQbU/AvP6sBjKuQSE4Ds9ADwP070P2PYNy0E0M9fcFlpiFMsckoiiHeFBPyRjIGrcljoZpqMXJoXidVFKamTgZWUZOME8gCfGBjHnVGXPLlbLIYlVIi7P21NRevWKWcKnKKFJe+ePBB6GoH/L6rmd5r5KgtqlpWoyxcR5HZVsYVuu2H38+dWNR1VLxUQzxIqgZjBA7x9vI/x70G3tXyUdmWrp6oR07fluo65h+s5/886SPLtPbLJRgo73RVFxp7HYa2pNvommgi7jNVN6oduwSq+Os4y3L561mgu5pqkOyVXJSyqy5kDe34PRPwNZrpwgpRXv7nLc5JvdH5r2a0o07JWzmitUkwSSQI0skZUFhxRcnkcf6/bOp0NttEdRcpbfJW2qeCWNqSKKrjqCjKchjKoHuHzgDycYxjW1PW1VC8dNKwVA3qIh6ZGPznyCcf46e9s7Zu+/5XslHapquCMJG91praKiWlLk+ms3Y/J5MzEgFxnIzjjrnv6HSTLXtt8o91bRo23NVXqXclGixGSpZJUqWCjMhYE5yMd5z1g40f2zYorhWwkLRwrFxGY0JZz/ANoyc6JfS7Y822rDt+zb7oLfZ4K01dRIsM4ChkMYj9RCoKyu0gBHgAD9QDF+3VfZrnbo9oW2S3BZmiiSjp1ZaxgSFIj48xke7BwTnOteLHLKqj+TNknGG2PNy2vtyltdZVXS1xwV0CJ+Y9P6jIpHTpB0WA6yfj5++qrodybRopI5zFcbvMI+JEUawRu3JvfjIA6xgYP66kRfUHcNbfaOuuYpaw00iD0paZSB0R4ZhgsPJBXOm+t2ptitoZ7ZJFR2u/A+2Zp/UjWXByeUbN7APPLoEjJwDrVHx44dZt39P9ZR8zybx/kDXH6rVlyijo7LE227KFjWVYzmoqWU9s0yj2+3oADK99nRTf8Avysa0bdh2iKhpadz+Kp1iNQ6Aj8pCzAlyqqxDHrDDrSDdrSu16uCk3G0dnqp15QpWTpH669kNH37l9p9wyOtNm2Kq/WS8PJbhNJOyKtRGwkfKAYUsU9wAyCMEfHxq3J4+PjeP0JDNNOp+x42v9QINxU9MJ2q7c9DTLUSLNBz5SKvBnRVIIXHfDJ78DTU24Kalt9FdbtUPBbqmZE9V4XgmPRIxCM8s8R2P06znQe9bnrKYUYsckMUssPoMIa2mjMTFSRzidwE93kkEqF+Sdb10iUdvgS4C10E9VE8k10oa6GkPL0yFfJzzJOCAhUdnkANcN1ezoq60FLt9QLRYoYf6bmr7Y9WCYYZLdlkQf2oVjlVz4JwT3jxrjQ7uS4Ws3X6fR2yrgoMQVNfc5Hp5IvALPFwJZGGSDyAyD9tUlafpXcd01NKLbf4b/WEfnhpnc04DY788gOslTjsasofTW1bask89wmtTXZkMKzK0jR8SAMiPkBI4wx+MHB7wRoOONaGi5djY+8rRt23LV394bhdquV5OM8C06+kP3mhHZK4/dIye/jS2v1/ireNNcbHPS08aEQR0UiN7/7NWLFeAII9/YU+dedt8bip63fEqJe6u9PRRLTLPUwmH0iqf1cSlmAjyfjHYJ+e13/i+SMTIZCI/SEYE4DdAgkddAk5OT1/no/Gmgpnsv8AB2Hc9ohrbVHVTipYmWWDm/HB9y4PfIEYYgY1mvLO0/qLNaKea0i7TUNmlPrrxqHgRJDgem4Ht7xnIwOtZqyKmlqQjjG/2iPYNtVu5/xcmKenoLbilhrJ4liMUtOpcqhfuNWeUcjnvCqfGNJlBR1V5moY6GeadpqqlWQRSFIGPq45OisGlXIxz/hwex516jP7Ocs9FcKanu+6YYrxN+JmcT0ddFLI+cTe9UkX5OR112Tgag0f7KlysFcl02zv6utEwhCtPUWGmqJeTEdeoJcd5GcAfH+KSnF+hlCSJdwgqZN10sE9tpqBIqWqkj9S3GeTDyxDJcHvoHBGQMn76N7jrrbab7IbHb6eS6VdGZ2lpz6DUjRgnI8H3IpzywV6IOCNS4Ppdu+m/Dfgr9SXSupaKGmarrVlT1eE/J2KKJBk/GBgN2c/Ou4Pp7v+S/VtSRS123RZmpzTvcEhrZKkyhg7syRgjhyQZbA5H9Na4+RC7b+1/wC/syvBOq/IBisV2vlPFW1sgV6pQ8Yq3OAhGf6wqQFGceRjX25WihsFmutJuSGmq66ajNTZ6SlrY1mrJlYIEgYN7+RPHGCAe8E4yn7z+oN8+mldbrJetnpTXqdzUSxz3iKpT8IpMa+6ByefJc+9V8OPdjIR6H6gTm32eqvNkoq5KGqKSV6184qHIm5mIes5VQVfiG7I4Kc5zm+flSlDimv4K4ePxlyaeha3rSU1Rf55Nw0m56aprqdfXS5Vfregw/qqeCRSUqIlAXgeKjA7ORgOv0ra53BW2PeNyXK3WipqhPRtXguqTOpBQxs6uAcntVYFvgfIOv3lR3qyGHdVoWnvDRQercqOUVTVEynmzSeo3sjkGF4whShUMWIYrpSqqy1zXik9Na+jpaIRilmZmjm+HMvFsYflnBGB0Dn7YG6do19o9RWL6GWuGtQXDdFSYnkKZobafzj17Y5GJUnv5HWNEqn6NXC1V6PTNVXChH5Yirb0lFKIz95QCvHvwhB+MedVbZv2j0namo77tJrlUtG3qz0Ffhp2C+0iJvBYjDnmBkkgD93RmX9ojbtp3j6FHtion2kaFOdbFTzfiYqw5Ln0nfgVBPA+Dn3AkdaLbe4sRWtNFtX36dRXO1UtBtthbKmof3o1ZNDSCNR8+lGMj+/IWJOTnSFbvpBexVx+taLXDQGUxio/HvPyJBOVSNmcnAyAQMjzpYP7WmzozSyXfbO5pKxZECLIyKqcmySuJCGIHYAVc4HjOhu7P2prjveelo/pHR3KlqI3aasqLjS0r+tEowsfpsxREGCWPIMcAZAB0q5rSH77QG3Fsa87U3XJVXoUdwp6uR5ozQSzMkL8gPTkWReUbMqgnkf4lwfjSVSzI0lK9Ypoo4JFaSRBzYorAjKnILY68YJHfWdXzNv/AHf9SrPUp/y1ElNSenOZbLeVaRgSqyqVSRuauAMqBkADzx15/vO6KF7pMv8AR62yNuNPNTUsZRcxnGGBy3POCeXedRNsdX7HrbstNvfe81vtVwpNjWu4IkUS1VO9a0rK/ML7FHKUtk/wrgcR9jmq+pd1zW6pgqLPJNQV8R5Q1EUxikib4ZWUhgf5H5/nrNK4yfQ6Zct5+tV2v1iqLDQVBtxnMapIKggosbc+IMbZXPHHWNEdnfVvcW2bJFQQUbX2ogmlJnqK6do+LNnCqVySBgcmJ8dY0mvf6KmyI6yRgmOqWCKEYA/uqf8AfXKbc1K5BhpfVyOnqpGfv56JI/01luUkXul2WLN9a94PHPFHRW+k9YMOQlk5pyXGO5Bn79jz/LSfVJuuu241kF3u39EPyJHrMTIGZSwdiDyU8QME4wSPnQdt01qY9CZKYKMYgjC6HT3Saqz+Imnnc9KZJCf/AJ0Upi2ibftq1G47i9y3PuWRrg8EcIlxCGSJCSqBFBA7Zj+73yJOSc6h021bPSxIj3K51HF/U4q6ogb7/uj7f66gyzNyIzx/QfOtoZUVZvXgkkcriLE3AK3/AFMMEn9Bkfr9tMuSXYrp+g4LXax6ckUM+IsIJJpQ4Axjj+7gDzorII7g1NJXk1CUsax04nAkVUGQAgcHC9YyMDSvRRRkSmsWaT8siIRzKgEngMxIPtHfQ7PQyNSULInZBwuBl/GM+P08/wCepxTCpNdMPvQ2idYqSot1D6BblxSER+4Ho5XB6Oo0uyrJVXmK4y+tBULIHjFM6xRk5JPtx5bOG77+NA5o5S64KAgD+0GilIryJxkKMCMH8wf/AHo7SFoYG2hT3DqhrXaQHAjmUTBf0I6Zf/YdIMFTabdWtUz2mjjK1j007KeaRyDKsMEceJxkqMDLZx2dND2154JYqmGGrpZIzE0b1HEhCO+Ljsfy8d+NV5X/AEMeveRtsXClmXHspKzCTqPsGU4fH3Az/d0cSjC90CSscLXuh7bVGshulp/EKxaKoNqo0wpz1goQcZwCP99ct2vFu6oFXcqmguVWiclmWGOKWXoELyjAz/Nsnod6TbB9G9522vFVYLlbaKsg8SLdfSeMg/ukMvXeDgjTjQ/Tze1Numo3Buiwbd3ULnGi3B5KyEOSpx6sbRAcHwBk8Ty+Rq1ySWmKouxcSjtiEs8EnqRgEGOpVcfyznWasus2FtSrZha6yss82SBDWohX5+TzH+ms1R8kh+BUQnmbyx76P6a7QvIT6cj4DeGJ8H76i5IkGD9v99dY+4wT2fvqxIjJSLKZFjZ1XkwXMknFVz8lvgfros9PaaGIqZ5L3VspGKcvBSxk+DzIEkrDvoBF8dnQimJMAJJJPk/fvXb+EfyGjQqPsULSlUj7lYhQB5bJ+/30xXHbtFZ6SOmqLkK2/SMHlhpWR6ajj/6Hk75ynyQh4r92OgA6YAdAkf76LRqsbMIwFAboAY0tBRrHQKBj1e8jJxroltWRgGn45+6akRIpiBKjPfeP11NgRTFGSoJAPZH89QNgx7Er9tOQQM44fH/mdTKawxMwP4liCQSOHnrvUtVGD0PP20NuM0iIOEjr38MRoEDcNhXLMap0Q9MPTXr/AAzrefbdFUA8rgicjkl1Xv8ATSjSSvKYPVdn5SqDyOc9nRy5U8MTQelFGnuP7qgfxHUoKCr19FBTm1bkllu1KB+RW02Fr6TByAshPGZB/wCnJkY/dZNfFtU8FvluO2Lo24bbSDnUTUlG3r06Hx+IpiTJH/3jlH9m+NLVMoZm5AHC4GR8Z8aB3C4VdnaKvtFVPQV0EsZhqaaUxSRkvg8WUgj/AAOpQRsF9t11iH4urhqQ4z6hp8swP651mmX6+22itu9KE26kp6Q1NvimnMMSp6sjH3O2B7mPyT2dZqJEP//Z", "target": "logo"}] }' -i -H "Content-type: application/json"
2.Response with results that are not so different from previous ones:
{"predictions":[{"filename":"120px-Abv.png","target":"logo","prediction":0.9994,"out_of_domain":0.0006},{"filename":"12_rue_de_Condé_-_detail.jpg","target":"logo","prediction":0.0024,"out_of_domain":0.9976},{"filename":"BackupVault_Logo_2019.png","target":"logo","prediction":0.989,"out_of_domain":0.011},{"filename":"Blooming_bush_(14248894271).jpg","target":"logo","prediction":0.0172,"out_of_domain":0.9828},{"filename":"Cambia_logo.png","target":"logo","prediction":0.9997,"out_of_domain":0.0003},{"filename":"Elizabeth_Drive_-_border_of_Edensor_Park_and_Bonnyrigg_Heights_in_New_South_Wales_62.jpg","target":"logo","prediction":0.003,"out_of_domain":0.997}]}
Change #1031590 had a related patch set uploaded (by Kevin Bazira; author: Kevin Bazira):
[machinelearning/liftwing/inference-services@main] logo-detection: process image objects instead of image URLs
Change #1031590 merged by jenkins-bot:
[machinelearning/liftwing/inference-services@main] logo-detection: process image objects instead of image URLs