czczup
Why BEiT_Adapter only have large size?
Hi ！ The research in this paper is an excellent work
By the way, I would like to ask why there are only BEiT_Adapter configs files with large scale whithout it's base scale results. And i manually wrote a config file of BEiT_Adapter_base on ade20k, but it's mIoU is just 53.54, while the origin BEiT_mask2former_base is 54.58. Why the results of base size didn't show in your paper? Does the performance of Adapter in base size with BEiT is not ideal ?
Apologies for the delay. I haven't tested BEiT_Adapter_base on ADE20K before, but I've just set up the experiment. I'll inform you as soon as the results are available.
Thank you for your patience.
This is my result of BEiT-Adapter-Base + Mask2Former. Note that the dimension of Mask2Former used in this experiment is 256. Could you provide your config of BEiT_mask2former_base for further alignment?
2023-09-30 11:56:04,746 - mmseg - INFO - Iter(val) [250] aAcc: 0.8501, mIoU: 0.5465, mAcc: 0.6819, IoU.wall: 0.7994, IoU.building: 0.8281, IoU.sky: 0.9472, IoU.floor: 0.8395, IoU.tree: 0.7659, IoU.ceiling: 0.8623, IoU.road: 0.8715, IoU.bed : 0.9187, IoU.windowpane: 0.6375, IoU.grass: 0.6844, IoU.cabinet: 0.6505, IoU.sidewalk: 0.6945, IoU.person: 0.8505, IoU.earth: 0.4393, IoU.door: 0.5317, IoU.table: 0.6926, IoU.mountain: 0.6409, IoU.plant: 0.5695, IoU.curtain: 0.7611, IoU.chair: 0.6512, IoU.car: 0.8682, IoU.water: 0.5808, IoU.painting: 0.7907, IoU.sofa: 0.7652, IoU.shelf: 0.4546, IoU.house: 0.4702, IoU.sea: 0.6439, IoU.mirror: 0.7254, IoU.rug: 0.6517, IoU.field: 0.3357, IoU.armchair: 0.5011, IoU.seat: 0.6643, IoU.fence: 0.4827, IoU.desk: 0.5617, IoU.rock: 0.5415, IoU.wardrobe: 0.5447, IoU.lamp: 0.7120, IoU.bathtub: 0.8853, IoU.railing: 0.4040, IoU.cushion: 0.6541, IoU.base: 0.3901, IoU.box: 0.3002, IoU.column: 0.5051, IoU.signboard: 0.4119, IoU.chest of drawers: 0.4841, IoU.counter: 0.3723, IoU.sand: 0.5475, IoU.sink: 0.7777, IoU.skyscraper: 0.4806, IoU.fireplace: 0.7571, IoU.refrigerator: 0.7965, IoU.grandstand: 0.5039, IoU.path: 0.3421, IoU.stairs: 0.3439, IoU.runway: 0.7621, IoU.case: 0.6224, IoU.pool table: 0.9437, IoU.pillow: 0.6268, IoU.screen door: 0.6726, IoU.stairway: 0.3060, IoU.river: 0.1128, IoU.bridge: 0.7292, IoU.bookcase: 0.3616, IoU.blind: 0.3939, IoU.coffee table: 0.6777, IoU.toil
et: 0.8649, IoU.flower: 0.5162, IoU.book: 0.5185, IoU.hill: 0.1149, IoU.bench: 0.4603, IoU.countertop: 0.6444, IoU.stove: 0.7822, IoU.palm: 0.5601, IoU.kitchen island: 0.5189, IoU.computer: 0.6937, IoU.sw
ivel chair: 0.4552, IoU.boat: 0.3621, IoU.bar: 0.5614, IoU.arcade machine: 0.5609, IoU.hovel: 0.4454, IoU.bus: 0.9196, IoU.towel: 0.7695, IoU.light: 0.6364, IoU.truck: 0.3794, IoU.tower: 0.2620, IoU.chand
elier: 0.7236, IoU.awning: 0.3449, IoU.streetlight: 0.3721, IoU.booth: 0.6130, IoU.television receiver: 0.7139, IoU.airplane: 0.7217, IoU.dirt track: 0.2041, IoU.apparel: 0.3526, IoU.pole: 0.3204, IoU.lan
d: 0.0352, IoU.bannister: 0.2305, IoU.escalator: 0.5250, IoU.ottoman: 0.4508, IoU.bottle: 0.4364, IoU.buffet: 0.4752, IoU.poster: 0.3954, IoU.stage: 0.2534, IoU.van: 0.4732, IoU.ship: 0.0261, IoU.fountain
: 0.2786, IoU.conveyer belt: 0.8157, IoU.canopy: 0.4949, IoU.washer: 0.8106, IoU.plaything: 0.3310, IoU.swimming pool: 0.6059, IoU.stool: 0.4643, IoU.barrel: 0.6915, IoU.basket: 0.4306, IoU.waterfall: 0.6
832, IoU.tent: 0.9524, IoU.bag: 0.1576, IoU.minibike: 0.7236, IoU.cradle: 0.8883, IoU.oven: 0.6189, IoU.ball: 0.4737, IoU.food: 0.6376, IoU.step: 0.1886, IoU.tank: 0.5566, IoU.trade name: 0.2809, IoU.micr
owave: 0.8365, IoU.pot: 0.4993, IoU.animal: 0.5931, IoU.bicycle: 0.5864, IoU.lake: 0.5583, IoU.dishwasher: 0.6504, IoU.screen: 0.5844, IoU.blanket: 0.2480, IoU.sculpture: 0.5268, IoU.hood: 0.7523, IoU.sco
nce: 0.5534, IoU.vase: 0.4784, IoU.traffic light: 0.4014, IoU.tray: 0.2017, IoU.ashcan: 0.4153, IoU.fan: 0.6863, IoU.pier: 0.5308, IoU.crt screen: 0.0162, IoU.plate: 0.5838, IoU.monitor: 0.1086, IoU.bulle
tin board: 0.3701, IoU.shower: 0.0269, IoU.radiator: 0.6873, IoU.glass: 0.2389, IoU.clock: 0.4590, IoU.flag: 0.5068, Acc.wall: 0.8778, Acc.building: 0.9112, Acc.sky: 0.9744, Acc.floor: 0.9155, Acc.tree: 0
.8762, Acc.ceiling: 0.9326, Acc.road: 0.9177, Acc.bed : 0.9644, Acc.windowpane: 0.7864, Acc.grass: 0.8285, Acc.cabinet: 0.7794, Acc.sidewalk: 0.8323, Acc.person: 0.9319, Acc.earth: 0.5723, Acc.door: 0.691
4, Acc.table: 0.8214, Acc.mountain: 0.8145, Acc.plant: 0.6980, Acc.curtain: 0.8805, Acc.chair: 0.7807, Acc.car: 0.9346, Acc.water: 0.7036, Acc.painting: 0.9076, Acc.sofa: 0.8962, Acc.shelf: 0.6070, Acc.ho
use: 0.7301, Acc.sea: 0.8402, Acc.mirror: 0.8304, Acc.rug: 0.7756, Acc.field: 0.5505, Acc.armchair: 0.6909, Acc.seat: 0.8507, Acc.fence: 0.6493, Acc.desk: 0.7741, Acc.rock: 0.7382, Acc.wardrobe: 0.7427, A
cc.lamp: 0.8249, Acc.bathtub: 0.9267, Acc.railing: 0.5550, Acc.cushion: 0.7764, Acc.base: 0.6289, Acc.box: 0.4274, Acc.column: 0.6184, Acc.signboard: 0.5792, Acc.chest of drawers: 0.6791, Acc.counter: 0.4
979, Acc.sand: 0.7746, Acc.sink: 0.8375, Acc.skyscraper: 0.5909, Acc.fireplace: 0.9410, Acc.refrigerator: 0.8982, Acc.grandstand: 0.7866, Acc.path: 0.5065, Acc.stairs: 0.4440, Acc.runway: 0.9675, Acc.case
: 0.8061, Acc.pool table: 0.9735, Acc.pillow: 0.7687, Acc.screen door: 0.7145, Acc.stairway: 0.4642, Acc.river: 0.2700, Acc.bridge: 0.8449, Acc.bookcase: 0.5272, Acc.blind: 0.4623, Acc.coffee table: 0.832
7, Acc.toilet: 0.9121, Acc.flower: 0.6664, Acc.book: 0.7746, Acc.hill: 0.1933, Acc.bench: 0.5640, Acc.countertop: 0.7720, Acc.stove: 0.8853, Acc.palm: 0.8074, Acc.kitchen island: 0.8554, Acc.computer: 0.7
741, Acc.swivel chair: 0.6416, Acc.boat: 0.5236, Acc.bar: 0.6841, Acc.arcade machine: 0.5941, Acc.hovel: 0.4681, Acc.bus: 0.9659, Acc.towel: 0.8593, Acc.light: 0.7791, Acc.truck: 0.5500, Acc.tower: 0.5328
, Acc.chandelier: 0.8545, Acc.awning: 0.4605, Acc.streetlight: 0.5291, Acc.booth: 0.7207, Acc.television receiver: 0.8858, Acc.airplane: 0.8224, Acc.dirt track: 0.3484, Acc.apparel: 0.5060, Acc.pole: 0.45
99, Acc.land: 0.0676, Acc.bannister: 0.3652, Acc.escalator: 0.7185, Acc.ottoman: 0.6483, Acc.bottle: 0.5931, Acc.buffet: 0.5879, Acc.poster: 0.5286, Acc.stage: 0.3521, Acc.van: 0.6482, Acc.ship: 0.0377, A
cc.fountain: 0.2830, Acc.conveyer belt: 0.9226, Acc.canopy: 0.6828, Acc.washer: 0.8371, Acc.plaything: 0.4871, Acc.swimming pool: 0.7623, Acc.stool: 0.7517, Acc.barrel: 0.7413, Acc.basket: 0.6218, Acc.wat
erfall: 0.9466, Acc.tent: 0.9803, Acc.bag: 0.2122, Acc.minibike: 0.8847, Acc.cradle: 0.9720, Acc.oven: 0.6964, Acc.ball: 0.5264, Acc.food: 0.8282, Acc.step: 0.3076, Acc.tank: 0.6555, Acc.trade name: 0.341
4, Acc.microwave: 0.9140, Acc.pot: 0.6053, Acc.animal: 0.6218, Acc.bicycle: 0.8026, Acc.lake: 0.6362, Acc.dishwasher: 0.7833, Acc.screen: 0.9112, Acc.blanket: 0.3279, Acc.sculpture: 0.8812, Acc.hood: 0.79
95, Acc.sconce: 0.7021, Acc.vase: 0.7047, Acc.traffic light: 0.6322, Acc.tray: 0.3209, Acc.ashcan: 0.6108, Acc.fan: 0.8137, Acc.pier: 0.8272, Acc.crt screen: 0.0552, Acc.plate: 0.7243, Acc.monitor: 0.1317
, Acc.bulletin board: 0.6315, Acc.shower: 0.2370, Acc.radiator: 0.8311, Acc.glass: 0.2797, Acc.clock: 0.6285, Acc.flag: 0.5645
Thanks to your reply! I uploaded my training results on https://github.com/cljhwt/Adapter/tree/4e36401b75c3a97bfe3016e978d6ffc4af02fc06 In which contains BEiT_mask2former_base configs and logs on 2 mmseg versions v1.0.0 and old 0.20.2. Due to limited experimental conditions, I trained these 2 models with batch_size=1 on 2 RTX3090. Could you provide your batch_size and training condictions of your previous base result?
Thanks to your reply! Could you please provide your BEiT-Adapter-Base + Mask2Former config file for my further alignment? Thanks