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Hi, @amaralibey thank you for share so great repo. Now i'm reproducing the paper's result, but i can't get same result as paper shows with just modify the data path in this repo. the best pitts250k test top1 recall is 92.85 as follows, not 94.6 in paper
Hi, @amaralibey thank you for share so great repo. Now i'm reproducing the paper's result, but i can't get same result as paper shows with just modify the data path in this repo. the best pitts250k test top1 recall is 92.85 as follows, not 94.6 in paper
and my parameters is here
agg_arch: MixVPR
agg_config:
in_channels: 1024
in_h: 20
in_w: 20
mix_depth: 4
mlp_ratio: 1
out_channels: 1024
out_rows: 4
backbone_arch: resnet50
batch_sampler: null
batch_size: 120
cities:
faiss_gpu: false
image_size: !!python/tuple
img_per_place: 4
layers_to_crop:
layers_to_freeze: 2
loss_name: MultiSimilarityLoss
lr: 0.05
lr_mult: 0.3
mean_std:
mean:
std:
milestones:
min_img_per_place: 4
miner_margin: 0.1
miner_name: MultiSimilarityMiner
momentum: 0.9
num_workers: 28
optimizer: sgd
pretrained: true
random_sample_from_each_place: true
show_data_stats: true
shuffle_all: false
val_set_names:
warmpup_steps: 650
weight_decay: 0.001
Do you mind to supply your parameters to reproduce your result? thank you
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