The logo-detection inference service takes Wikimedia Commons image(s) and returns a prediction on whether they are a logo or not.
- Model Card: TBD
- Source: https://gitlab.wikimedia.org/mfossati/scriptz/-/blob/main/liftwing_prototype.py
- Paper: https://arxiv.org/abs/2104.00298
- Model: https://analytics.wikimedia.org/published/wmf-ml-models/logo-detection/
- Model license: Apache 2.0
Note
Unfortunately, tensorflow-cpu is not available for apple silicon. If you are a Mac user, please replace tensorflow-cpu with tensorflow in logo_detection/model_server/requirements.txt.
In order to run the logo-detection model server locally, please choose one of the two options below:
1. Automated setup using the Makefile
In the first terminal run:
make logo-detectionThis build process will set up: a Python venv, install dependencies, download the model(s), and run the server.
On the second terminal query the isvc using:
curl -s localhost:8080/v1/models/logo-detection:predict -X POST -d '{"instances": [ { "filename": "Cambia_logo.png", "image": "iVBORw0KGgoAAAANSUhEUgAAAHgAAABACAYAAADRTbMSAAAABGdBTUEAALGPC/xhBQAAAAFzUkdCAK7OHOkAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAAAZiS0dEAP8A/wD/oL2nkwAAAAlwSFlzAAALEgAACxIB0t1+/AAAIExJREFUeNrtnXd4HOd95z/vzM72XfTeQXSAnRJFU4VqsRXLiuQSOXaSi+P0XJrzJE6UplxxnORJPdvnx4nrneOzlJN1sWxaIil2CmAFQZAACBC9l93F9p3Zmbk/drEECIBis0I5/PEhCWBmZ2ff7/v+yvf3fQfCNE2Te/Yja9K9IbgH8D0DNEPn3ejs7gF8AzYXDfMnR7/P4bEr77p7t9yDb30zTJP2yWE+d/Yo+0cuY5Mt7KmsuwfwtaabBppuYJEkLNK7w2mYpsm/DXTzZ8f3Mh0OZtz0vRWctoimcmlhmnMz45yfnWQmGiLH7uQDG1p5rKoet2K7qwfm0NgALxx5DV88iixJGKZJgdN9D+DZaJiDo/281HuOi/PTBBIxDNNEACZwcLSfZ+s28pmH349VvnsjhMdqJ8fuxBePAmC3KGwqKP2PC/BiIs7p6VG+3NXOkfEraIaOJETm75IFE3EuzE8S15N3NcDbisrZWlRGv38OIQSKJJFlc9yRa6t6amzeiXB12yNsmCbnZyf45652vj94iVhSS8ew1Io1038kRGag7i+uwqVY7+7yQghy7c6liIxVlrFbbm+44kmNfxvo5vDYFT7esp33lNXc3QBHNZX/fek0nzt7lNloOPPzEpeX+0ur2JhfgkuxMhMN8crlLkaCPkpcXj7Wsh1Z3P3JVrknB0kITBOcFivZt7GCw5rK17tP8oVzx2jMLaTck313u+hAPMZn2vfxf3rPktCTyEKiyOXh/bUtfLxlB/U5BStcUFNuEb978FU+1rydhtzCd0X8KnS6sUgSScMAAQJxS9eJJTX+smM/X+lq5/6SKr7w5EcodnnuXoAXYhE+076Pb/WcRTcNcmxOnm3YyCc3PkB1Vi6KJK96TW12HnU5BTzfvA1ZiHcFwFFNxbhN9ippGHyr5yzfuHgKp2LlP7XdT9E7BO4tATwS9PPi8b28PtSLYZrUZuXz4u738VhV/ZrALlme3cWvbd1NmTvrXZOBToQX0U0js3JNbh7so+NX+GzHfuJJjRyXk00FpbyT0/umAB4PBfidN7/DiYkhTEx2llTxFw9/gJb84re96TJPFmWeje+qEmP5Z9IN46aJjoSe5KXeThYTMUDwUHktpe/wBL/hTCesJfjHM0cy4G4pLOMvHv4ArTcA7rvVarPzM15JCIF0k4lhLKkxtLiAaZpIQrCrrPq2M/Efygo2TJNvdJ/iWz1nAKj05vBb2x+hNb+YH2VbnipIQmC5SYCzbQ5+smkrk+FFWvKKebSy/u4kOi7OT/Ol82+hGSk++adbdvBUbTM/6iYJ6WrmbIJxCzH4Yy3b2VNRR47dQU6mrr6LXHQ0qfGl8yeYjgQRAp7e0MonNu685ZLh3WSFTjeKLK0Zk2/U7LKFMk/WCjbvZiykJgiq8R/OCjZNk//VfYp/G+gGoMDh5te3PoTXar/rwdFNg6lwkDdH+9mQncfustrbzriWT+qQmmA2GqLPN0simaTUnUWpJ4tyT9aK845PDPE/zx0jrCX47w89fUNhLaEnuTA3xWtXLtI5O45umjyzoY2Pt+7AaVHuHMBXAvP8U9dbJPQkFknik5t2/VDibkJP4otHsUoyuQ7nbXuHWFLj690n+Xr3SYYXffxs2323BLDgGnJDpK69d7CHl/s6ubQwjT8ewzRNbBYLpW4vv3f/43xgQytJw2DvUA+feWsfVwJzgODMzNh1x880TTrnJvlKVzv7hvuYi4URpLj887MTTEeC/P7Ox7HdBId/3TP3DvUwGV7ExGRbUTk/3bL9ll3Nqg+DyXQkxOHRAX4w1Euvbwa3YuPn2u7n+eat162p327lfuVCB3/VcYCEnkx1sta4537/HO2TI1Rn5fBgWe2a5yiSjCwkTEzsssKl+Wle7uvk9aFeIpqKEKnkSyBIagY9CzN84dwx9lTU0T45zAtHXmMuGsYiyRim+baSn9PTY/za/pcZDfppzSvmmbo2Li1Mc2ZmHE3X+frFU+yprOeh8trbB3h40ce3e89hmCaKJPPBhs3kOVx3BNyQGudr3Sf5177z9Plm0QwdWUgI4M+O7yWiqfzi5l23NJmOjg/yP84eQdWTSEKkW5Urr3Nk7Ap/cuz79PpmqPLm8s2nf4b6nIJV19IMHd00kBDMRkN8+vB3GQv5cSs23lNWQ31OPnkOF9+9cpHhwAIWSeayb5Yz02NsKizlz3c/xYvH9zK3jKe/XqXyzZ4zDAYW+IVNu/jtHY9Q4vIyHgrwWwde4fjEECE1wb/2dbKzpAqrLN86wKZp8o2LpxjwzwPQmFvI+2qa7szKNU2+2HmCvz19iGybnac3tFKbnceh0QG656cIayr/1HWCJ6ob2JCdv+br+/3zzMXCOCwKrfnFGZel6jrf7jmHLxbN8OAWSaJhGXiqnuTrF09yaWEaRZKZiYaYCC+uCfDSCkYIgmqCQDxGbVYen7rvUX68thmXYkMSgqbcIj518Duouk40qTERXmRPZR07S6qwWSyY6ZJLvk57UBKC7UUV+ONRfnHTLkpcXgDKPdl8vGUHp6ZHUXWd4xNDDC0u0HiDfP6aAA8uLmQSK0kInm/aSnH6DW/XgmqcfcN9eK02/v6xD/J4VQMWSeLDjVv45N5v0e+fYzIc5ODowCqADdPkWz1n+bvTh5iPRbBbLPz+/Y/xiY0PIICJcICT0yOpDlA6hpqslNqcn53kxMRQZgKoepJ+/xx7KlZrrfKdbrLs9kwW+2B5LX/+4FO05pes8Ak7S6sodnkZXvRhkSScyupEyCpZyH8bD/jxlu0837QV5ZrVeV9xBcUuL6NBPxPhRU5Nj94ewGdnxpmOpHRIeQ4XD1dsuGMJldtq49M7HyeW1Hissj4z0A05BTxR1UC/fw7dMDg02s9Pt2zHvixrDKsJzs2OY5VlEkmNqKbyct95Pty4Ba/Vnr7vEC7FilOxMh+LoBsGI0F/ZoK83NeJLx7LNDwM08QXi66b/AkEJibFLi9/vOvHaMsvWZPQKHJ6GAwsoEjysj7y1YlZ5PLQnFf8NnV36r0uzU9xenqMAf88VlkmoqlENRUBGKZB5+wEH23aiuUG8pRVACcNgx8M9aDqOkLAhuw8ytzZdwxgWUg8XtWw5rGHKzbw1e4O4skkvb4ZZqIhqry5meMem52/fOQZXu3v4rcPfAfT0AnEY0Q0Fa/VTtfcJJqu87HmbVgkia9c6EAIMkqMoBrn/NwEpmliCpHJkj1W25rx94vnjjEa9GfOKfWszSMnDQNVT2JiUu7Jvuru06WViUmO3UGWzX7dGHwqrYg5NjHIQjSSanQIgWmaWCQ5ldCZ8OZIP6PBALXZeTcP8GIixmR4MU3TCR6pqFtzAG7HIppKn2+W4xNDDC8uAGCTLcxGwxn9VkhNMBkOrgBYALIQ1OcU4LZaWYhFybY7cCs2FmIRjoxdwaVY+UjjFg6NDaSZHEFhWizXMTlCv3+OxtxCIprKVHgRSQiy7asb+a9duchrg5cQIqVOUXU91RdeJ+zMxyIprsDpvrqCzVS1INIArpdFa4bOl7va+cK5Y0yFg1R4s3m+eSsNOYW4FCtjoQCHxgboWZhGEoK5aIiT0yO3BvD/G7jApfkZJCGhXJOg3K4lDYP2yWG+dP4E7VMjGdcoCYEQqeNWWUYIQSARY99wH7tKq1ddp8Tlpcqby3wsQlRT0QydoUUf46EAO4oracor4l8vn8c0wSLLFDjcqLrOV7s7MEz41H172DvYw6v9ASxCrHLRvniUL3YeJxCPYZFSZZIkBNI69fnwog9fPIpAkGWzr5FMpVahuWZZl8or/vrkm0Q1lR+raeKFB56kKa9wheql/EI2Lxx5DSFSk23vYA/P1W9625rYcm3MOTszQVzXkBBk25zU3yH1RdIw+Fp3B39z6iBz0QhVWTk8W7eRupx8smwOEnqSU1Oj7B/pI5CIAaSz6sQqia1DUTKarpCaIKwm2D/Sx2IizpPVDXitdnTDAEwskkSuw0nH1Ainp8d4tn4jT29opWNyJHNfPQszGOmOD8BY0M/Ioh+bbMEmy4TTjf/1uOiuuUmimkqWzc7HmrdnangDk6VFmzTNVeIB0zT5du9ZPtO+j6Aa58nqRv7h8efId7jfNlZ3zk4wFFigKa/oxgH2xaKcmR5FIDAwKXV7KXTcvhZYM3S+0X2Kz3YcIKwmeKyqnj/Y+QRbi8pXrIln6tq47J/DNzuOhKBnYYaxYIDmaz6EYZgZcZ9LsRJPapyYGMKtWNmYlrY6FSsmYLdYkIXEN3tPI4CfadmRSoQczoz7DWuJVL0r5IzLjSU1GnMLqcvJ55XLXalm/xr4xpNJjo8PohkGtdn5bCwoWXughbSqrj86Pshn2/fjj0fxWu18qGHzuuAO+Oeu5g5CMBsJ8b3BS28L8ApfMh0J4o/HMh+81J2F+zbjr24afO3CydQsTcRpyy/hbx59lm3XgAvgj0eZj6boOSEEC7EIb00OrbqmQ1HId7gwTJNsm4PFRJzBwALl3uxMaWWXlTTNJ3FqepQDI/08VL4hQxXeV1y5wgvoy+Jr0jDQTYONBSVsKSy77uebCAe47J8DYHdZzYpyUmRKtZQnWS5Vimoqnz93jOlICBD8ZNNWnqlrW/M9JsOLtE+NIITAaVEQIrUAD472s5iI3zjAY6EAYS2RYX7cVhuydHvU5OtDvfz1qTfTma6NX9j0ABXrKAonQov4E7EMbZiK2SOrlBSKJLO9uAJJCGRJ0D0/xUIsQnNuUSbBcVgsCJFKGv/xzBEiWoIPNWzKlF1lnmzcij1TfsWTycz1a7LyKPdkE9FU8h3uVF26Dst4cHSAiVCAck8WT1xTHcSSGqqhIxAk9CTasknUOTtB5+w4kkhl6O+vbVmXnj05Ncpl3ywOi8LzTVvxKDYkBN3z0+wf6btxgP3x6IpM0WGx3BbxH9VU/uXSGQJpQv6Ryjqerd+4box+7cpFopqKnBbLy0Lw1uQwfb7ZNWrPVFMipCY4OTWCLEm8t6Y5U1fbLakVbJgmgUSU2qy8FTsT3IoVR1pd4Y9HCWuJzLHqrFx+Y9tDDAYW8Cei6yYyM5EQ//fyeUzgExt3rtI5i/ToLXmjmUgoc6zPN5tZfZXenHVdbTyZ5I3hXhJ6kuqsXD6xcSe7yqoxMIlqKq8P9WbC1dsCPJFuLCy5l9vdedDnm+XU9BiyEDgVKx9q2LyCuLi2c7Uk5NtTUUelNweTlILz2PjgqvN98QhCpDLYA6P95DtcK9zptc2D99U2U+HNyXzvtdmpTH8fS2qreq7PN23jc098iB1FFRQ43WsmWN/uPcfZmXE2FZTy0eZtqyeh3Z7WUpssJuJMhhczx0aCPkxSiVdddj4562iu94308fpQL1bZwk81b6M+p4CnapqxSDKyJPHGcC9f7DyObhrXB9jEZDoczGR9siStyQXfjE2EF4kmVUxSorttReXrxulv9ZxlKhIkz+HkV7fuZndZDWZ6Ze8fubxqllqklNoiltRYjMcocXvXVEzopkFNVh4/UbfSc7gVG09WNyEJQURTWUiXSoZpopupmNmUV8TmwjJ2lVavqmFPTAzxte4OHBYLv7T5PRQ5PWt4MI1IUkUg0Ayd8VAgw5n3++cxzdRCimgqyTUAOj09xl91HCCkxnlfTRPPN21LE0J1tOYVY5gG8WSSL50/wXcud9Hvn1vV2JCWuxOvzZ5xyCag3Obembcmh1GTKYYn1+7Cs45Q4NDoAC/1ngPgw41beE9pDe+tacZhsSALwcX5afYNr4w1XuuyGS8Ej1c2kLOMsLDLFnTTYGthOX/32HM0rVHubS4sxWu1E0tqdM9PAXBsfJDPnz2amVCSEDxZ3YhVthDXtUz8/PTh7zIeWuSjTdv4QF3rdRmqpf+/cfEUb472IwSZUCKEoN8/x2iaTl3KCb556TS/tu9len0zPFXbwou7n8owYcUuDz/bel8qkUxzBp8+/F2e+86Xee3KxfVddKpzcrVGW4+5udHseS4aTpP+Ip3IaKvOeWO4lz8++j3mYxF2l9Xyn7c+hBCCtvxiarPyMdPEw192HODC3NSKDPXqfYtVW0EeLK/lN7c9zN8+9iy7SqvX7PdWe3MpdntJGganpkYwTJNIUuVL50+wd7An4/a2FpbjVKycn53kjeE+fvPAK1wJzPNcwyZ+Z8eedWN0ls1OicubIUouzk/zxnAvspAy4UESgvFQgBeOvMaXu9r5clc7v/zGS/zhkdcYCfp4qqaF//rgj69KTD/YuJln6zeiGwaGaRJSE7Tll/BY1Uphn/ziiy++uERy/HNXO0OLvkw3Zk9l/bpu9UYz6EsL00iSRDypUZudz4bsPJKGyZXAPP9w5gh/e/ogE+EgH27czH958KkMUG6rDc3QOTQ2gCQEvniUztkJzs2Mo+o6CT3JvuE+DEzyHClR/fIdA3kOF49U1F13T6/baiOe1Dg2MUhQTfBAaTWbCkrZO9TDq/0XGAn6sUgysaTGRChAx9QIX73QwUQ4wM+07uC/Pfj+6/bI7RaFoBrn2PgQhmnywcbNvPDAE7itNhwWhaNjVzKJ1mjQz4GRy+wb6WPAP0++080nNz7AH+16kpI1tNSKJGdyDosk8VzDZv5g5xMr8gwAsfwxSp8/d5S/aN+fjkMGv771If70Pe+9ZYC/3n2SF45+L1WgmyY5DicNOYVIQjAS9DEa9ONSbPxEXRt/+MCTq/brTIaD/Pzef+HszDgWSUIzdDxWO599+GkSus6nDn6HptwifmXLbj7SuOWGm+AreOREnC90HuPLXe205pfwuSc+xJGxK/zhkdeIaCouxYpNthDREmiGTp7DxYcaNvO79z16QyrJQCLG60O9BBIxnqvfSOGyWP1q/wX+8ewRFhMxfGlevcjpoS4nn0+07WRLUfnbbvPRTYOopuJWbGt6qRW+xWlZuaXzwtxU5kPeiv1E3UaOjF3he4OXMIH5aITZyCAmJg6Lws6SKn5+4wM8VduMY43sutTt5c92v48/Ofp9+vyzNOcV8bv3PcpTtc20T47wTF0bv7Njz5otvBs1r83O793/GE25Rbzc18lUJMhHmragGjov9Z6j3z+HVZapzy1na2EZH2zYTFt+yQ1Ppmybg+ebtq557Jm6Nh4sryGiqVwJLFDgcFPlzcGhKDcsWZKFtG5us2oF7x/p4xd/8O2Mlqnck80rz/58Jl7cik1Fgrw+1MNsNEzH5AiV3pz0LC3gsar6Vb3T9cj8SwvTbCkso8SdlenOqLp+x3YKmKaJZugospxp8QUTcXp9s7gVG5XeHFyK9Y5p0t4pWwHw4bEBfu77/0JCT2b8/B/t+jF+afOuOzKA0aSGVZZvWVB3z27eVmTRxS4vHqstU/PFkxpvjl4moqk3feGQmuBKYJ7pSDCtSRLpFSAR1VT88RhBNZ4Rtt17ouIPx1b4twpPNluKynl9qAc5rce9MDdF78IM24srbvii05Egf3psL+2Tw1R4cvj8kx+mOiuXWFLjc2ePcmh0gLiuYZcVCl1uFEnGYVEocXspcnpwKVbKPNkUOd04MnmBiSLLeKx2bLLlHXeVScNIdZk07Tr+MOX1FEki2+bAIKX5UtPPI7HKKflsIB5Lc9RglWWssoWopqHqSQKJGJqupx93IeO2WrFKMrIk47XacCjKTdHHK5MsxcqjFXW8MdSbKcIXYhH+4cxh/v7xD95QvBwN+vn04e9yaGwA3TCwSHKG/pSFxObCUoJqnPbJYU5Oj2RIljRfkRF6OywKbqvtajJjpsqOQqebMk82pW4v+Q4XVtmS2b2Xa3elmwyCCk8ObmtKlyUJgWYYzESCzEbDqHqSmUiIkJqgJisvIzJIGgbBRJxQul1olS24FStxPclI0MdQwMeiGme9zoNAYJctOBUrzXlFxJIac9EwYU3FaVEodHnQDYN+/xyxpIZIl1IuxUogESORTBJNqiSNlFTHIiRs6XanVZYpcnqoyc6jJisXr9WO12anyOmh2J3yvB7FvionEdc+bXYiFOA3DrzCiYmhzCoRQvD+NJtS6l6trjSBYCLGobEBvnqhg46pUQzTxCbLfGrHHn5j+8OrnskxGV7kn7vaeeXyeWYiIQzM9GavlQzQWrHcXDYZrq0Nl+45z+HCqSj4YlGEEOlyQstop8y0nEZKCadYavcaprlio7dYBqdY/q+49r6uNuOXnqtlpIkSiyQj0sekZWI/WUiZ1uySRGrpnKX2pSxJWNM5i5TewipEar+y3aKQNHScihW3YmNXaTW/v/PxFVWPWOtxwj8Y6uGXX38p40aWQLy/pJKPNm1jU0EpkhAE1RSB3jkzQefcBJ0zE0STKookU+rO4mMt2/nVLbvXLIGWarg+3yz7hy/zg6Eeen2zRLQEFiFT4vYSUuPEk0mShp4e+GtWTLrjJKeb6WLZzRrpL5ZPghRFmHKhspBQZJmkbiAJgSLLuK02XEshQZAZWIsk47XZM/uCIppKWEug6Xpm4PMcLvIcLso92ZS4vJn61AS86bBiky04FIWEniSiqnhttlXKyCWAl1hEqyxnxi/l/mXMtOu3yhYC8SjRpIZppgifhtyCFYtpTYB98Si/8vpLHB4bWKEvWiLhc2wOhBBENTXDKpmmiZzWcP1U8zaerG6iNjvvhmKlSapve25mnK65SdyKjYfKNxDREszFIgTiMeK6liHn4+n3c1lTM9epWHEpyg1t0LbKMjbZkon7cT2JLAR2WcFusVylHQWZ/cBL7lKSUi18PS0IyAxd+vFQd+OTg8R6DwTvnp/iU2++yvm5ifTMuuo+l9ykmf5akWV2lVbz9IZWHqmooyYr9176ercDvNSj/ealM4wEfZyaGmU+FsnEKJtsocKTQ212Hk9WN/L0htYbSsLu2V0EMMsYo46pEaYjQcJqgkhSpSYrj+1FFeQ5nDe1nfFO2nzAz6xvgZbaldtOhicnEELgdbmZmp9ddfxGbc7vY2BslPvbNl13X9GdNNM0icRjuOwOYok4F68MsKWxGeUWGbu3fZUkBHaLhUfu4PaVO2Vzfh/dAwOrAByaHEcIwaa6RtTr1a1vY7phEFcTV1Pkd8DiqsreY0d4764HkWWZhKreFgn07/o0UMM06R7op294CKfdzgObNqNYLHQP9KMlk0zNz1FfWcW2phYWw2FOXbzAfMBPXlY2D23bgSQk/KEge48fIRyN0lhdy6b6hrQWSiDLEnabnWg8Tt/wIOFojInZaWrKKtjR2kZCTdBxoYs5v4/C3Dwcdjvbmloyq8Uiy7gcTmKJON0D/SQ0jan5OapKSrm/dSM9w4P4g4ssBAIIIXjP5q0U5OQyOj3Fud5LqJpGU3UtTTW1nO29RG1ZOQU5uYzNTLMYClFdVsap7gvMLMyT7fFyf9smeocH6RkaJDcrmx0trTgdDiRJYta3wOlL3YSiUSqLS9jR0sa838+V8VEi8TiLoRBbGptoqKpen6p8p+3y8BDnei/RtqEOm9XKG28dZ87v5wcnjmGaJk3VNRw81cH4zDQdF84TjcfZ0dLGxNws3QOXkSWJmYV5crxZ1FdWc+zcGYYnJ5CklOzWt7hIz+AAsUSc1986jmkaNNXUcvJiF/N+HyfOdzLjW2BTQyMTc7McPn2SpK4v8xB+2rs6CUWj7Ot4C1XTaN1QR8eF84zPztB1uY/Ovl4aq2tI6jr7O95izu/jlTf3kePNoqGqmsNnT9E/OsL4zDRd/ZdTXbr+yyyGQ3T3XyYYCbM9/Zne6uqkMDcPj8tFUW4ecVXl9MVuAqEgrx48gCQkNtY1cKH/MqcvdTM5P8ubpzrIz86mpKCA7x8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", 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", "target": "logo" } ] }' -i -H "Content-type: application/json"If you would like to remove the setup run:
MODEL_TYPE=logo-detection make clean2. Manual setup
First add the top level directory of the repo to the PYTHONPATH:
export PYTHONPATH=$PYTHONPATH:.Create a virtual environment and install the dependencies using:
python3 -m venv .venv
source .venv/bin/activate
pip install -r logo_detection/model_server/requirements.txtDownload the logo_max_all.keras model from the link below and place it in the same directory named PATH_TO_MODEL_DIR.
https://analytics.wikimedia.org/published/wmf-ml-models/logo-detection/
We can run the server locally with:
MODEL_PATH=PATH_TO_MODEL_DIR MODEL_NAME=logo-detection python3 logo_detection/model_server/model.pyOn a separate terminal we can make a request to the server with:
curl -s localhost:8080/v1/models/logo-detection:predict -X POST -d '{"instances": [ { "filename": "Cambia_logo.png", "image": 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