This project provides an API for vectorizing images using OpenAI's CLIP model using BentoML. It allows you to send images to the API and receive a vector representation that encodes meaningful features of the image.
The processor automatically resizes images to the model's expected input dimensions (e.g., 224x224 for this model) and normalizes pixel values.
- Easy-to-use REST API for image vectorization.
- Powered by the OpenAI CLIP model.
- Serves vectorization directly from BentoML.
- Ready for local testing.
The service supports the following image formats based on Pillow's capabilities:
| Format | File Extensions | Description |
|---|---|---|
| JPEG | .jpg, .jpeg, .jpe |
Common format with lossy compression, widely used for photographs. |
| PNG | .png |
Lossless compression, supports transparency (alpha channel). |
| BMP | .bmp, .dib |
Bitmap image format, uncompressed. |
| GIF | .gif |
Supports animation and transparency; only the first frame is processed. |
| TIFF | .tiff, .tif |
Flexible format supporting multiple layers and compression options. |
| PPM | .ppm, .pgm, .pbm |
Portable Pixmap formats (NetPBM). |
| ICO | .ico |
Icon format, often used for application icons. |
| WEBP | .webp |
Modern image format for web usage, supports both lossy and lossless modes. |
| DDS | .dds |
DirectDraw Surface, used for textures in graphics applications. |
| TGA | .tga |
Targa format, often used in video games and graphics. |
| HDR | .hdr |
High Dynamic Range image format, used for realistic lighting. |
| JPEG 2000 | .jp2, .j2k, .jpx |
Advanced JPEG format with better compression. |
- Create and activate a Python virtual environment:
python3 -m venv .venv source .venv/bin/activate - Install dependencies:
python3 -m pip install transformers Pillow torch bentoml
- Start the BentoML service:
bentoml serve service:svc
- Verify the service is running:
- Health Check:
curl -v http://127.0.0.1:3000/livez
- Metrics:
curl http://127.0.0.1:3000/metrics
- Health Check:
Send an image to the API and receive a vector representation:
curl -X POST -H "Content-Type: image/jpeg" --data-binary @image.jpg http://127.0.0.1:3000/vectorizeThe response will be a JSON object containing the image vector:
{"vector":[-0.6687430143356323,-0.022710340097546577,-0.15384919941425323,-0.2727549970149994,0.4632737934589386,0.16514046490192413,0.09627659618854523,0.6447893977165222,0.4654671251773834,0.02930556796491146,-0.020234737545251846,0.13032759726047516,-0.542938232421875,-0.6593307256698608,-0.18386724591255188,0.07420549541711807,-0.020180068910121918,-0.13537637889385223,0.1709609478712082,-0.28379443287849426,-0.09426041692495346,0.3305407166481018,-0.4156511723995209,0.17745189368724823,0.3153534233570099,-0.02357916720211506,0.35171350836753845,0.2296837866306305,0.8345032930374146,-0.2707275450229645,0.19283245503902435,-0.4151032269001007,-0.44615352153778076,-0.3210270404815674,0.3318961560726166,0.4676361083984375,-0.4021487832069397,0.23209887742996216,-0.3185116648674011,0.7966142296791077,-0.2598493993282318,0.35262978076934814,0.3129599690437317,-0.17915745079517365,0.28403380513191223,-1.9820750951766968,-0.008681900799274445,0.39878207445144653,-0.4686589539051056,0.25742441415786743,-0.14648295938968658,-0.1207902729511261,0.08937818557024002,-0.25353747606277466,-0.536446213722229,0.12080903351306915,0.3860810399055481,-0.3868846595287323,-0.1369106024503708,-0.3076460361480713,0.4234112799167633,-0.397183895111084,-0.09535045921802521,0.03087746538221836,0.02453356608748436,0.08965231478214264,-0.7286188006401062,0.7265434861183167,-0.12212776392698288,-0.32552242279052734,-0.4765230119228363,-0.24616089463233948,0.16469888389110565,-0.017333701252937317,-0.6357914805412292,-0.06779493391513824,-0.14376555383205414,0.5299485921859741,0.17236435413360596,-0.40594810247421265,-0.259965717792511,0.2835208773612976,-0.08705392479896545,0.07975983619689941,0.24842654168605804,0.08793193101882935,0.07566756755113602,-0.05389964580535889,0.50528484582901,-0.33495745062828064,-0.2059670239686966,-0.023608390241861343,-7.187380790710449,0.10389988869428635,-0.4126972556114197,0.11890425533056259,0.1492355465888977,-0.4540826678276062,-0.5568014979362488,-0.1283746063709259,0.1250491589307785,0.3253483772277832,0.11105281859636307,0.09786208719015121,0.19193975627422333,-0.15640385448932648,-1.1143189668655396,0.19040577113628387,-0.1052761971950531,-0.013899981044232845,0.21917814016342163,0.6362742781639099,-0.24634525179862976,-0.0022915152367204428,0.07358803600072861,0.060874905437231064,-0.12489165365695953,0.2864362895488739,0.2366129755973816,-0.36690008640289307,-0.08417553454637527,-0.41571661829948425,0.0265496876090765,-0.06496751308441162,0.18957103788852692,-0.012186618521809578,0.2702372968196869,0.3300398886203766,0.23428553342819214,0.12994609773159027,-0.21896624565124512,-0.5408898591995239,-0.12862853705883026,0.8993349075317383,-0.10929622501134872,-0.16828884184360504,-0.05212879180908203,-0.39806613326072693,-0.28135260939598083,-0.049532048404216766,-0.18422460556030273,0.24449452757835388,-0.42220720648765564,0.041131943464279175,-0.2257036417722702,-0.20136742293834686,-0.2935275733470917,-0.35853374004364014,-0.32096046209335327,0.11864367127418518,0.49388977885246277,-0.15340092778205872,0.28780031204223633,-0.22219887375831604,0.47611895203590393,-0.07730628550052643,-0.2743844985961914,-0.2890458405017853,-0.04988916218280792,-0.22355858981609344,0.12299978733062744,-0.26488932967185974,-0.18939678370952606,-0.1037522554397583,-0.17824573814868927,-0.0656704232096672,0.3611837327480316,0.5910423994064331,0.23946894705295563,-0.018466074019670486,-0.2885538637638092,-0.011474087834358215,0.1443667709827423,-0.04336171969771385,0.05861891433596611,0.08690109103918076,-0.38174137473106384,-0.03723884001374245,-0.9623693823814392,0.20358490943908691,-0.13544803857803345,0.010264777578413486,0.13430726528167725,-0.09786717593669891,0.1561468094587326,0.46919846534729004,-0.498098224401474,-0.16015055775642395,0.33450761437416077,-0.4248847961425781,0.2797245383262634,-0.0552232451736927,0.023255901411175728,0.4122523367404938,0.001955569488927722,-0.28531312942504883,-0.14753109216690063,0.17971429228782654,-0.010966102592647076,0.5021719932556152,0.05363306403160095,0.6847209334373474,-0.2836933135986328,0.5522644519805908,-0.016495738178491592,-0.45481076836586,-0.22258462011814117,-0.10553868114948273,0.08228834718465805,0.23540951311588287,-0.6100614666938782,0.4537906050682068,-0.512470006942749,-0.3049924671649933,0.15360650420188904,-0.03903038799762726,0.552315890789032,0.49156108498573303,-0.07450329512357712,-0.24077649414539337,-0.04143289104104042,-0.13271500170230865,-0.05301252380013466,0.3722735345363617,-0.15448760986328125,0.07515329122543335,-0.0459660179913044,-0.290149986743927,0.13526467978954315,0.03725915402173996,0.1986769586801529,0.17600400745868683,0.19316820800304413,-0.47234046459198,-0.13859936594963074,-1.0071736574172974,-0.3022332191467285,0.29640182852745056,0.11855960637331009,0.07150129228830338,0.5902463793754578,-0.25115805864334106,-0.21826037764549255,0.35356080532073975,-0.4266735017299652,-0.4658615291118622,-0.008019642904400826,0.5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# Use Bento to make a container image
bentoml build
# Re tag the image so it isn't random
bentoml containerize clip_image_vectorizer:latest -t bentoml:latest
docker run --rm -p 3000:3000 bentoml:latest