├── download_data.sh ├── evaluation ├── demo_eval_cls.m └── eval_cls.m ├── COPYRIGHT ├── categories_extra69.txt ├── categories_places365.txt └── README.md /download_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | echo "Downloading data split for Places365-Standard" 3 | wget http://data.csail.mit.edu/places/places365/filelist_places365-standard.tar 4 | tar -xvf filelist_places365-standard.tar 5 | 6 | echo "Downloading data split for Places365-Challenge" 7 | wget http://data.csail.mit.edu/places/places365/filelist_places365-challenge.tar 8 | tar -xvf filelist_places365-challenge.tar 9 | -------------------------------------------------------------------------------- /evaluation/demo_eval_cls.m: -------------------------------------------------------------------------------- 1 | %this script demos the usage of evaluation routines 2 | % the result file 'demo.val.pred.txt' on validation data is evaluated 3 | % against the ground truth 4 | 5 | fprintf('PLACES365 SCENE CLASSIFICATION TASK\n'); 6 | 7 | pred_file='demo.val.pred.txt'; 8 | ground_truth_file='../data/places365_val.txt'; 9 | num_predictions_per_image=5; 10 | 11 | fprintf('pred_file: %s\n', pred_file); 12 | fprintf('ground_truth_file: %s\n', ground_truth_file); 13 | 14 | error_cls = eval_cls(pred_file,ground_truth_file,1:num_predictions_per_image); 15 | 16 | disp('# guesses vs cls error'); 17 | disp([(1:num_predictions_per_image)',error_cls']); 18 | 19 | 20 | -------------------------------------------------------------------------------- /evaluation/eval_cls.m: -------------------------------------------------------------------------------- 1 | function [cls_error] = eval_cls(predict_file, gtruth_file, num_pred_per_image) 2 | 3 | preds = cell(1, 5); 4 | [predlist, preds{1}, preds{2}, preds{3}, preds{4}, preds{5}] = textread(predict_file, '%s %d %d %d %d %d'); 5 | preds = cell2mat(preds); 6 | 7 | [filelist, labels] = textread(gtruth_file, '%s %d'); 8 | 9 | assert(length(filelist)==length(predlist), 'number of predictions does not match number of ground truth'); 10 | 11 | filemap = containers.Map(predlist, 1:length(predlist)); 12 | pred_idx = cell2mat(filemap.values(filelist)); 13 | 14 | assert(length(pred_idx)==length(unique(pred_idx)), 'predictions are not unique'); 15 | 16 | preds = preds(pred_idx, :); 17 | 18 | for i=1:length(num_pred_per_image) 19 | n = num_pred_per_image(i); 20 | cls_error(i) = 1 - mean(any(preds(:, 1:n)==repmat(labels, [1 n]), 2)); 21 | end 22 | -------------------------------------------------------------------------------- /COPYRIGHT: -------------------------------------------------------------------------------- 1 | Copyright (c) 2015 Places365 2 | 3 | Permission is hereby granted, free of charge, to any person 4 | obtaining a copy of this software and associated documentation 5 | files (the "Software"), to deal in the Software without 6 | restriction, including without limitation the rights to use, 7 | copy, modify, merge, publish, distribute, sublicense, and/or sell 8 | copies of the Software, and to permit persons to whom the 9 | Software is furnished to do so, subject to the following 10 | conditions: 11 | 12 | The above copyright notice and this permission notice shall be 13 | included in all copies or substantial portions of the Software. 14 | 15 | Neither name of copyright holders nor the names of its contributors 16 | may be used to endorse or promote products derived from this software 17 | without specific prior written permission. 18 | 19 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 20 | EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES 21 | OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND 22 | NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT 23 | HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 24 | WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING 25 | FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR 26 | OTHER DEALINGS IN THE SOFTWARE. 27 | 28 | -------------------------------------------------------------------------------- /categories_extra69.txt: -------------------------------------------------------------------------------- 1 | /t/toll_plaza 0 2 | /b/baggage_claim 1 3 | /d/dentists_office 2 4 | /l/lido_deck/outdoor 3 5 | /h/hot_tub/outdoor 4 6 | /d/dining_car 5 7 | /v/videostore 6 8 | /c/cheese_factory 7 9 | /c/courtroom 8 10 | /e/elevator/interior 9 11 | /g/great_hall 10 12 | /t/teashop 11 13 | /l/labyrinth/outdoor 12 14 | /r/ranch_house 13 15 | /p/promenade_deck 14 16 | /w/warehouse/indoor 15 17 | /v/volleyball_court/indoor 16 18 | /m/music_store 17 19 | /l/limousine_interior 18 20 | /w/wine_cellar/barrel_storage 19 21 | /t/tennis_court/outdoor 20 22 | /f/firing_range/indoor 21 23 | /t/tennis_court/indoor 22 24 | /c/casino/indoor 23 25 | /s/subway_interior 24 26 | /w/wine_cellar/bottle_storage 25 27 | /b/badminton_court/indoor 26 28 | /o/optician 27 29 | /b/basketball_court/outdoor 28 30 | /s/squash_court 29 31 | /h/hat_shop 30 32 | /a/athletic_field/indoor 31 33 | /c/cybercafe 32 34 | /l/loft 33 35 | /e/electrical_substation 34 36 | /t/thriftshop 35 37 | /f/factory/indoor 36 38 | /s/shower_room 37 39 | /b/bow_window/outdoor 38 40 | /n/nuclear_power_plant/outdoor 39 41 | /a/anechoic_chamber 40 42 | /b/batters_box 41 43 | /c/chicken_coop/outdoor 42 44 | /h/hot_tub/indoor 43 45 | /e/editing_room 44 46 | /o/observatory/outdoor 45 47 | /d/dinette/vehicle 46 48 | /r/rest_area 47 49 | /p/portrait_studio 48 50 | /c/covered_bridge/exterior 49 51 | /f/funeral_home 50 52 | /k/kennel/indoor 51 53 | /p/power_plant/outdoor 52 54 | /w/walk_in_freezer 53 55 | /o/oil_refinery/outdoor 54 56 | /f/forest/needleleaf 55 57 | /f/florist_shop/outdoor 56 58 | /l/liquor_store/outdoor 57 59 | /j/jail/indoor 58 60 | /p/poolroom/home 59 61 | /d/driving_range/outdoor 60 62 | /b/brewery/indoor 61 63 | /o/outhouse/outdoor 62 64 | /p/podium/indoor 63 65 | /t/theater/indoor_seats 64 66 | /f/fitting_room/interior 65 67 | /a/aquatic_theater 66 68 | /p/podium/outdoor 67 69 | /s/synagogue/indoor 68 -------------------------------------------------------------------------------- /categories_places365.txt: -------------------------------------------------------------------------------- 1 | /a/airfield 0 2 | /a/airplane_cabin 1 3 | /a/airport_terminal 2 4 | /a/alcove 3 5 | /a/alley 4 6 | /a/amphitheater 5 7 | /a/amusement_arcade 6 8 | /a/amusement_park 7 9 | /a/apartment_building/outdoor 8 10 | /a/aquarium 9 11 | /a/aqueduct 10 12 | /a/arcade 11 13 | /a/arch 12 14 | /a/archaelogical_excavation 13 15 | /a/archive 14 16 | /a/arena/hockey 15 17 | /a/arena/performance 16 18 | /a/arena/rodeo 17 19 | /a/army_base 18 20 | /a/art_gallery 19 21 | /a/art_school 20 22 | /a/art_studio 21 23 | /a/artists_loft 22 24 | /a/assembly_line 23 25 | /a/athletic_field/outdoor 24 26 | /a/atrium/public 25 27 | /a/attic 26 28 | /a/auditorium 27 29 | /a/auto_factory 28 30 | /a/auto_showroom 29 31 | /b/badlands 30 32 | /b/bakery/shop 31 33 | /b/balcony/exterior 32 34 | /b/balcony/interior 33 35 | /b/ball_pit 34 36 | /b/ballroom 35 37 | /b/bamboo_forest 36 38 | /b/bank_vault 37 39 | /b/banquet_hall 38 40 | /b/bar 39 41 | /b/barn 40 42 | /b/barndoor 41 43 | /b/baseball_field 42 44 | /b/basement 43 45 | /b/basketball_court/indoor 44 46 | /b/bathroom 45 47 | /b/bazaar/indoor 46 48 | /b/bazaar/outdoor 47 49 | /b/beach 48 50 | /b/beach_house 49 51 | /b/beauty_salon 50 52 | /b/bedchamber 51 53 | /b/bedroom 52 54 | /b/beer_garden 53 55 | /b/beer_hall 54 56 | /b/berth 55 57 | /b/biology_laboratory 56 58 | /b/boardwalk 57 59 | /b/boat_deck 58 60 | /b/boathouse 59 61 | /b/bookstore 60 62 | /b/booth/indoor 61 63 | /b/botanical_garden 62 64 | /b/bow_window/indoor 63 65 | /b/bowling_alley 64 66 | /b/boxing_ring 65 67 | /b/bridge 66 68 | /b/building_facade 67 69 | /b/bullring 68 70 | /b/burial_chamber 69 71 | /b/bus_interior 70 72 | /b/bus_station/indoor 71 73 | /b/butchers_shop 72 74 | /b/butte 73 75 | /c/cabin/outdoor 74 76 | /c/cafeteria 75 77 | /c/campsite 76 78 | /c/campus 77 79 | /c/canal/natural 78 80 | /c/canal/urban 79 81 | /c/candy_store 80 82 | /c/canyon 81 83 | /c/car_interior 82 84 | /c/carrousel 83 85 | /c/castle 84 86 | /c/catacomb 85 87 | /c/cemetery 86 88 | /c/chalet 87 89 | /c/chemistry_lab 88 90 | /c/childs_room 89 91 | /c/church/indoor 90 92 | /c/church/outdoor 91 93 | /c/classroom 92 94 | /c/clean_room 93 95 | /c/cliff 94 96 | /c/closet 95 97 | /c/clothing_store 96 98 | /c/coast 97 99 | /c/cockpit 98 100 | /c/coffee_shop 99 101 | /c/computer_room 100 102 | /c/conference_center 101 103 | /c/conference_room 102 104 | /c/construction_site 103 105 | /c/corn_field 104 106 | /c/corral 105 107 | /c/corridor 106 108 | /c/cottage 107 109 | /c/courthouse 108 110 | /c/courtyard 109 111 | /c/creek 110 112 | /c/crevasse 111 113 | /c/crosswalk 112 114 | /d/dam 113 115 | /d/delicatessen 114 116 | /d/department_store 115 117 | /d/desert/sand 116 118 | /d/desert/vegetation 117 119 | /d/desert_road 118 120 | /d/diner/outdoor 119 121 | /d/dining_hall 120 122 | /d/dining_room 121 123 | /d/discotheque 122 124 | /d/doorway/outdoor 123 125 | /d/dorm_room 124 126 | /d/downtown 125 127 | /d/dressing_room 126 128 | /d/driveway 127 129 | /d/drugstore 128 130 | /e/elevator/door 129 131 | /e/elevator_lobby 130 132 | /e/elevator_shaft 131 133 | /e/embassy 132 134 | /e/engine_room 133 135 | /e/entrance_hall 134 136 | /e/escalator/indoor 135 137 | /e/excavation 136 138 | /f/fabric_store 137 139 | /f/farm 138 140 | /f/fastfood_restaurant 139 141 | /f/field/cultivated 140 142 | /f/field/wild 141 143 | /f/field_road 142 144 | /f/fire_escape 143 145 | /f/fire_station 144 146 | /f/fishpond 145 147 | /f/flea_market/indoor 146 148 | /f/florist_shop/indoor 147 149 | /f/food_court 148 150 | /f/football_field 149 151 | /f/forest/broadleaf 150 152 | /f/forest_path 151 153 | /f/forest_road 152 154 | /f/formal_garden 153 155 | /f/fountain 154 156 | /g/galley 155 157 | /g/garage/indoor 156 158 | /g/garage/outdoor 157 159 | /g/gas_station 158 160 | /g/gazebo/exterior 159 161 | /g/general_store/indoor 160 162 | /g/general_store/outdoor 161 163 | /g/gift_shop 162 164 | /g/glacier 163 165 | /g/golf_course 164 166 | /g/greenhouse/indoor 165 167 | /g/greenhouse/outdoor 166 168 | /g/grotto 167 169 | /g/gymnasium/indoor 168 170 | /h/hangar/indoor 169 171 | /h/hangar/outdoor 170 172 | /h/harbor 171 173 | /h/hardware_store 172 174 | /h/hayfield 173 175 | /h/heliport 174 176 | /h/highway 175 177 | /h/home_office 176 178 | /h/home_theater 177 179 | /h/hospital 178 180 | /h/hospital_room 179 181 | /h/hot_spring 180 182 | /h/hotel/outdoor 181 183 | /h/hotel_room 182 184 | /h/house 183 185 | /h/hunting_lodge/outdoor 184 186 | /i/ice_cream_parlor 185 187 | /i/ice_floe 186 188 | /i/ice_shelf 187 189 | /i/ice_skating_rink/indoor 188 190 | /i/ice_skating_rink/outdoor 189 191 | /i/iceberg 190 192 | /i/igloo 191 193 | /i/industrial_area 192 194 | /i/inn/outdoor 193 195 | /i/islet 194 196 | /j/jacuzzi/indoor 195 197 | /j/jail_cell 196 198 | /j/japanese_garden 197 199 | /j/jewelry_shop 198 200 | /j/junkyard 199 201 | /k/kasbah 200 202 | /k/kennel/outdoor 201 203 | /k/kindergarden_classroom 202 204 | /k/kitchen 203 205 | /l/lagoon 204 206 | /l/lake/natural 205 207 | /l/landfill 206 208 | /l/landing_deck 207 209 | /l/laundromat 208 210 | /l/lawn 209 211 | /l/lecture_room 210 212 | /l/legislative_chamber 211 213 | /l/library/indoor 212 214 | /l/library/outdoor 213 215 | /l/lighthouse 214 216 | /l/living_room 215 217 | /l/loading_dock 216 218 | /l/lobby 217 219 | /l/lock_chamber 218 220 | /l/locker_room 219 221 | /m/mansion 220 222 | /m/manufactured_home 221 223 | /m/market/indoor 222 224 | /m/market/outdoor 223 225 | /m/marsh 224 226 | /m/martial_arts_gym 225 227 | /m/mausoleum 226 228 | /m/medina 227 229 | /m/mezzanine 228 230 | /m/moat/water 229 231 | /m/mosque/outdoor 230 232 | /m/motel 231 233 | /m/mountain 232 234 | /m/mountain_path 233 235 | /m/mountain_snowy 234 236 | /m/movie_theater/indoor 235 237 | /m/museum/indoor 236 238 | /m/museum/outdoor 237 239 | /m/music_studio 238 240 | /n/natural_history_museum 239 241 | /n/nursery 240 242 | /n/nursing_home 241 243 | /o/oast_house 242 244 | /o/ocean 243 245 | /o/office 244 246 | /o/office_building 245 247 | /o/office_cubicles 246 248 | /o/oilrig 247 249 | /o/operating_room 248 250 | /o/orchard 249 251 | /o/orchestra_pit 250 252 | /p/pagoda 251 253 | /p/palace 252 254 | /p/pantry 253 255 | /p/park 254 256 | /p/parking_garage/indoor 255 257 | /p/parking_garage/outdoor 256 258 | /p/parking_lot 257 259 | /p/pasture 258 260 | /p/patio 259 261 | /p/pavilion 260 262 | /p/pet_shop 261 263 | /p/pharmacy 262 264 | /p/phone_booth 263 265 | /p/physics_laboratory 264 266 | /p/picnic_area 265 267 | /p/pier 266 268 | /p/pizzeria 267 269 | /p/playground 268 270 | /p/playroom 269 271 | /p/plaza 270 272 | /p/pond 271 273 | /p/porch 272 274 | /p/promenade 273 275 | /p/pub/indoor 274 276 | /r/racecourse 275 277 | /r/raceway 276 278 | /r/raft 277 279 | /r/railroad_track 278 280 | /r/rainforest 279 281 | /r/reception 280 282 | /r/recreation_room 281 283 | /r/repair_shop 282 284 | /r/residential_neighborhood 283 285 | /r/restaurant 284 286 | /r/restaurant_kitchen 285 287 | /r/restaurant_patio 286 288 | /r/rice_paddy 287 289 | /r/river 288 290 | /r/rock_arch 289 291 | /r/roof_garden 290 292 | /r/rope_bridge 291 293 | /r/ruin 292 294 | /r/runway 293 295 | /s/sandbox 294 296 | /s/sauna 295 297 | /s/schoolhouse 296 298 | /s/science_museum 297 299 | /s/server_room 298 300 | /s/shed 299 301 | /s/shoe_shop 300 302 | /s/shopfront 301 303 | /s/shopping_mall/indoor 302 304 | /s/shower 303 305 | /s/ski_resort 304 306 | /s/ski_slope 305 307 | /s/sky 306 308 | /s/skyscraper 307 309 | /s/slum 308 310 | /s/snowfield 309 311 | /s/soccer_field 310 312 | /s/stable 311 313 | /s/stadium/baseball 312 314 | /s/stadium/football 313 315 | /s/stadium/soccer 314 316 | /s/stage/indoor 315 317 | /s/stage/outdoor 316 318 | /s/staircase 317 319 | /s/storage_room 318 320 | /s/street 319 321 | /s/subway_station/platform 320 322 | /s/supermarket 321 323 | /s/sushi_bar 322 324 | /s/swamp 323 325 | /s/swimming_hole 324 326 | /s/swimming_pool/indoor 325 327 | /s/swimming_pool/outdoor 326 328 | /s/synagogue/outdoor 327 329 | /t/television_room 328 330 | /t/television_studio 329 331 | /t/temple/asia 330 332 | /t/throne_room 331 333 | /t/ticket_booth 332 334 | /t/topiary_garden 333 335 | /t/tower 334 336 | /t/toyshop 335 337 | /t/train_interior 336 338 | /t/train_station/platform 337 339 | /t/tree_farm 338 340 | /t/tree_house 339 341 | /t/trench 340 342 | /t/tundra 341 343 | /u/underwater/ocean_deep 342 344 | /u/utility_room 343 345 | /v/valley 344 346 | /v/vegetable_garden 345 347 | /v/veterinarians_office 346 348 | /v/viaduct 347 349 | /v/village 348 350 | /v/vineyard 349 351 | /v/volcano 350 352 | /v/volleyball_court/outdoor 351 353 | /w/waiting_room 352 354 | /w/water_park 353 355 | /w/water_tower 354 356 | /w/waterfall 355 357 | /w/watering_hole 356 358 | /w/wave 357 359 | /w/wet_bar 358 360 | /w/wheat_field 359 361 | /w/wind_farm 360 362 | /w/windmill 361 363 | /y/yard 362 364 | /y/youth_hostel 363 365 | /z/zen_garden 364 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Places365 Data Development Kit 2 | 3 | ## Introduction 4 | 5 | This is the documentation of the Places365 data development kit. If you want the Places-CNN models instead of the training data, please refer to the [Places365-models](https://github.com/CSAILVision/places365). 6 | 7 | Table of contents: 8 | - Overview of Places Database 9 | - Image data details for Places365-Standard and Places365-Challenge 10 | 1. Image list and annotations 11 | 2. Submission format 12 | 3. Evaluation routines 13 | - Overview of the Places-extra69 data 14 | 15 | Please contact Bolei Zhou (bzhou@csail.mit.edu) for questions, comments, or bug reports. 16 | 17 | ## Downloads 18 | 19 | * First download the image list and annotations for [Places365-Standard](http://data.csail.mit.edu/places/places365/filelist_places365-standard.tar) and the image list and annotations for [Place365-Challenge](http://data.csail.mit.edu/places/places365/filelist_places365-challenge.tar), and decompress the files in the data folder. This file only contains image list, without actual images. 20 | 21 | * Download the corresponding compressed image files at [here](http://places2.csail.mit.edu/download.html). This file contains the actual images of Places database. 22 | 23 | ## Overview of Places365-Standard Data 24 | 25 | The category list of the Places365 is at [categories_places365.txt](categories_places365.txt). There are three types of image data for Places365-Standard: training data from Places365-Standard (TRAINING), validation data (VALIDATION), and test data (TEST). There is no overlap in the three sources of data: TRAINING, VALIDATION, and TEST. All three sets of data contain images of 365 categories of scenes. 26 | 27 | Number of images 28 | 29 | Dataset TRAIN VALIDATION TEST 30 | ----------------------------------------------- 31 | Places365-Standard 1,803,460 36,500 328,500 32 | 33 | 34 | Every image in training, validation and test sets has a single image-level label specifying the presence of one scene category 35 | 36 | Places365-Standard statistics: 37 | 38 | Training: 39 | 40 | - 1,803,460 images, with between 3,068 and 5,000 per category 41 | 42 | Validation: 43 | 44 | - 36,500 images, with 100 images per category 45 | 46 | Test: 47 | 48 | - 328,500 images, with 900 images per category 49 | 50 | Packaging details: 51 | 52 | The 3 sets of images (training, validation and test) are available as 53 | 3 tar archives. All images are in JPEG format. We provide both the original 54 | images and images resized to 256*256 for download. 55 | 56 | ## Overview of Places365-Challenge Data 57 | 58 | There are three types of image data for this competition: training data from Places365-Challenge (TRAINING), validation data (VALIDATION), and test data (TEST). There is no overlap in the three sources of data: TRAINING, VALIDATION, and TEST. All three sets of data contain images of 365 categories of scenes. The VALIDATION and TEST are the same as the Places365-Standard. The first 5000 images (or less as it is bounded by the total image number in that category) in each category are the images from Places365-Standard train set. 59 | 60 | Number of images 61 | 62 | Dataset TRAIN VALIDATION TEST 63 | ----------------------------------------------- 64 | Places365-Challenge 8,026,628 36,500 328,500 65 | 66 | 67 | Every image in training, validation and test sets has a singleimage-level label specifying the presence of one scene category 68 | 69 | Places365-Challenge statistics: 70 | 71 | Training: 72 | 73 | - 8,026,628 images, with between 3068 and 40,000 per category 74 | 75 | Validation: 76 | 77 | - 36,500 images, with 100 images per category 78 | 79 | Test: 80 | 81 | - 328,500 images, with 900 images per category 82 | 83 | Packaging details: 84 | 85 | The 3 sets of images (training, validation and test) are available as 86 | 3 tar archives. All images are in JPEG format. We provide both the original 87 | images and images resized to 256*256 for download. 88 | 89 | 90 | ## Details of the data 91 | 92 | The 365 scene categories used in the challenge dataset are part of the Places2 dataset. 93 | 94 | All the class names and ids are available in: [categories_places365.txt](categories_places365.txt), where each line contains the scene category name followed by its id 95 | (an integer between 0 and 364). 96 | 97 | The difference betweee Places365-Challenge and Places365-Standard is 98 | that there are ~6.2million more extra images in Places365-challenge 99 | compared to Places365-standard. The first 5000 images (or less) per 100 | category in Places365-challenge belong to the Places365-standard. 101 | 102 | ### 1 Training data 103 | 104 | Each image is considered as belonging to a particular scene category. 105 | See [1] for more details of the collection and labeling strategy. 106 | 107 | The training images may be downloaded as a single tar archive. 108 | Within it there is a tar file for each alphabet from 'a.tar' to 109 | 'z.tar'. Note that there are 24 such files as there are no scene 110 | category names beginning with 'q' and 'x' in our database. 111 | 112 | After untarring all of the above files, the directory structure should 113 | look similar to the following: 114 | a/abbey/00000000.jpg 115 | a/abbey/00000001.jpg 116 | ... 117 | z/zen_garden/00009067.jpg 118 | z/zen_garden/00009068.jpg 119 | 120 | In general, each leaf folder contains one scene category. Note that there 121 | are some categories that are fine-grained, e.g., s/swimming_pool/indoor 122 | and s/swimming_pool/outdoor. The complete list of training images and their 123 | mapping to scene category ids is available in: 124 | data/places365_train_challenge.txt 125 | 126 | All images are in JPEG format. We also include the data/places365_train_standard.txt 127 | here, you don't need to use it. 128 | 129 | ### 2 Validation data 130 | 131 | There are a total of 36,500 validation images. They are named as 132 | 133 | Places365_val_00000001.jpg 134 | Places365_val_00000002.jpg 135 | ... 136 | Places365_val_00036499.jpg 137 | Places365_val_00036500.jpg 138 | 139 | There are 100 validation images for each scene category. 140 | 141 | The classification ground truth of the validation images is in 142 | data/places365_val.txt, 143 | 144 | where each line contains one image filename and its corresponding scene 145 | category label (from 0 to 364). 146 | 147 | ### 3 Test data 148 | 149 | There are a total of 328,500 test images. The test files are named as 150 | 151 | Places365_test_00000001.jpg 152 | Places365_test_00000002.jpg 153 | ... 154 | Places365_test_00328499.jpg 155 | Places365_test_00328500.jpg 156 | 157 | There are 900 test images for each scene category. The ground truth 158 | annotations will not be released. 159 | 160 | 161 | ## Submission format 162 | 163 | The submission of results on test data will consist of a text file 164 | with one line per image, in the alphabetical order of the image file 165 | names, i.e. from Places365_test_00000001.jpg to 166 | Places365_test_00328500.jpg. Each line contains up to 5 detected 167 | scenes, sorted by confidence in descending order. 168 | 169 | The format is as follows: 170 | 171 | 172 | 173 | The predicted labels are the scene categories ( integers between 0 and 174 | 364 ). The number of labels per line must be exactly equal to 5, or it 175 | would lead to an error. The filename is the same as mentioned above, 176 | e.g., 'Places365_test_00000001.jpg' and so on. 177 | 178 | Example file on the validation data is 179 | 180 | evaluation/demo.val.pred.txt 181 | 182 | 183 | ## Evaluation routines 184 | 185 | The Matlab routine for evaluating the submission is 186 | 187 | ./evaluation/eval_cls.m 188 | 189 | To see an example of using the routines, start Matlab 190 | in the 'evaluation/' folder and type 191 | demo_eval_cls; 192 | 193 | and you will see something similar to the following output: 194 | 195 | PLACES365 SCENE CLASSIFICATION TASK 196 | pred_file: demo.val.pred.txt 197 | ground_truth_file: ../data/places365_val.txt 198 | 199 | ### guesses vs cls error 200 | 1.0000 0.9974 201 | 2.0000 0.9944 202 | 3.0000 0.9920 203 | 4.0000 0.9893 204 | 5.0000 0.9867 205 | 206 | In this demo, we take top i ( i=1...5) predictions (and ignore the 207 | rest) from your result file and plot the error as a function of the 208 | number of guesses. 209 | 210 | Only the error with 5 guesses will be used to determine the winner. 211 | 212 | (The demo.val.pred.txt used here is a synthetic result.) 213 | 214 | ## Overview of the Places-Extra69 Data 215 | 216 | Totally there are 434 scene categories in the Places Database. Besides the data of the Places365 we released above, here we release the data of the extra 69 scene categories. The category list of the extra 69 categories is at [here](categories_extra69.txt), where each line contains the scene category name followed by its id (an integer between 0 and 68). Download the images at the [project page](http://places2.csail.mit.edu/download.html). There are the splits of train and test in 217 | the compressed file. For each category, we leave 100 images out as the test images. There are 98,721 images for training and 6,600 images for testing. For those categories which don't have 100 enough images, we don't include them in the testing split. 218 | 219 | Potentially this Places-extra69 data could be used for one-shot learning or few-shot learning, or some transfer learning research. 220 | 221 | 222 | ## Reference 223 | 224 | Link: [Places2 Database](http://places2.csail.mit.edu), [Places1 Database](http://places.csail.mit.edu) 225 | 226 | Please cite the following [IEEE Transaction on Pattern Analysis and Machine Intelligence paper](http://places2.csail.mit.edu/PAMI_places.pdf) if you use the data or pre-trained CNN models. 227 | 228 | ``` 229 | @article{zhou2017places, 230 | title={Places: A 10 million Image Database for Scene Recognition}, 231 | author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba, Antonio}, 232 | journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 233 | year={2017}, 234 | publisher={IEEE} 235 | } 236 | 237 | ``` 238 | 239 | ## License 240 | 241 | The pre-trained Places-CNN models can be used under the Creative Common License (Attribution CC BY). Please give appropriate credit to our work, such as providng a link to the paper or the [Places project page](http://places2.csail.mit.edu). The copyright of all the images from the Places and Places2 database belongs to the image owners. 242 | --------------------------------------------------------------------------------