haqishen/Google-Landmark-Recognition-2020-3rd-Place-Solution haqishen/Google-Landmark-Recognition-2020-3rd-Place-Solution

  • Stars
    star
    144
  • Rank 255,590 (Top 6 %)
  • Language
    Jupyter Notebook
  • License
    Apache License 2.0
  • Created about 4 years ago
  • Updated over 3 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
haqishen/Google-Landmark-Recognition-2020-3rd-Place-Solution
github

haqishen

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Google Landmark Recognition 2020 Competition Third Place Solution

competition: https://www.kaggle.com/c/landmark-recognition-2020/

paper: https://arxiv.org/abs/2010.05350

solution summary: https://www.kaggle.com/c/landmark-recognition-2020/discussion/187757

HARDWARE: (The following specs were used to create the original solution)

Ubuntu 18.04.3 LTS

Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz (80 cores)

8 x NVIDIA Tesla V100 32G

SOFTWARE (python packages are detailed separately in requirements.txt):

Python 3.6.10

CUDA Version 11.0.194

nvidia Driver Version: 418.116.00

Data preparation

  1. Download the Google Landmarks Dataset v2 to ./data using the scripts at https://github.com/cvdfoundation/google-landmark This is our training data.

  2. Download the label csv file at https://s3.amazonaws.com/google-landmark/metadata/train.csv and put it in the same directory as train folder

  3. Download ReXNet_V1-2.0x pretrained model weights from https://github.com/clovaai/rexnet and put it in ./rexnetv1_2.0x.pth

  4. Run python preprocess.py It will read ./train.csv, create folds and save ./train_0.csv for training, and save ./idx2landmark_id.pkl to be used by the submission kernel.

Training

Training commands of the 9 models.

After training, models will be saved in ./weights/ Tranning logs will be saved in ./logs/ by default.

data_dir=./data
model_dir=./weights

### B7 

python -u -m torch.distributed.launch --nproc_per_node=6 train.py --kernel-type b7ns_DDP_final_256_300w_f0_10ep --train-step 0 --data-dir ${data_dir} --image-size 256 --batch-size 42 --enet-type tf_efficientnet_b7_ns --n-epochs 10 --CUDA_VISIBLE_DEVICES 0,1,2,3,4,5 --fold 0 

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b7ns_DDP_final_512_300w_f0_40ep --train-step 1 --data-dir ${data_dir} --image-size 512 --batch-size 16 --enet-type tf_efficientnet_b7_ns --n-epochs 40 --stop-at-epoch 13 --fold 0 --load-from ${model_dir}/b7ns_DDP_final_256_300w_f0_10ep_fold0.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b7ns_final_672_300w_f0_load13_ep20 --train-step 2 --data-dir ${data_dir} --init-lr 5e-5 --image-size 672 --batch-size 10 --enet-type tf_efficientnet_b7_ns --n-epochs 20 --stop-at-epoch 1 --fold 0 --load-from ${model_dir}/b7ns_DDP_final_512_300w_f0_40ep_fold0.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b7ns_final_672_300w_f0_load13_load1_14ep --train-step 3 --data-dir ${data_dir} --image-size 672 --batch-size 10 --enet-type tf_efficientnet_b7_ns --n-epochs 14 --stop-at-epoch 4 --fold 0 --load-from ${model_dir}/b7ns_final_672_300w_f0_load13_ep20_fold0.pth

### B6

python -u -m torch.distributed.launch --nproc_per_node=4 train.py --kernel-type b6ns_DDP_final_256_300w_f1_10ep --train-step 0 --data-dir ${data_dir} --image-size 256 --batch-size 64 --enet-type tf_efficientnet_b6_ns --n-epochs 10 --fold 1 --CUDA_VISIBLE_DEVICES 0,1,2,3

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b6ns_DDP_final_512_300w_f1_40ep --train-step 1 --data-dir ${data_dir} --image-size 512 --batch-size 22 --enet-type tf_efficientnet_b6_ns --n-epochs 40 --stop-at-epoch 28 --fold 1 --load-from ${model_dir}/b6ns_DDP_final_256_300w_f1_10ep_fold1.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b6ns_final_768_300w_f1_load28_5ep_1e-5 --train-step 2 --data-dir ${data_dir} --init-lr 1e-5 --image-size 768 --batch-size 10 --enet-type tf_efficientnet_b6_ns --n-epochs 5 --fold 1 --load-from ${model_dir}/b6ns_DDP_final_512_300w_f1_40ep_fold1.pth

### B6

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b6ns_DDP_final_512_300w_f1_40ep --train-step 1 --data-dir ${data_dir} --image-size 512 --batch-size 22 --enet-type tf_efficientnet_b6_ns --n-epochs 40 --stop-at-epoch 36 --fold 1 --load-from ${model_dir}/b6ns_DDP_final_256_300w_f1_10ep_fold1.pth

### B5

python -u -m torch.distributed.launch --nproc_per_node=4 train.py --kernel-type b5ns_DDP_final_256_300w_f2_10ep --train-step 0 --data-dir ${data_dir} --image-size 256 --batch-size 64 --enet-type tf_efficientnet_b5_ns --n-epochs 10 --fold 2 --CUDA_VISIBLE_DEVICES 0,1,2,3

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b5ns_DDP_final_576_300w_f2_40ep --train-step 1 --data-dir ${data_dir} --image-size 576 --batch-size 24 --enet-type tf_efficientnet_b5_ns --n-epochs 40 --stop-at-epoch 16 --fold 2 --load-from ${model_dir}/b5ns_DDP_final_256_300w_f2_10ep_fold2.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b5ns_final_768_300w_f2_load16_20ep --train-step 2 --data-dir ${data_dir} --init-lr 5e-5 --image-size 768 --batch-size 13 --enet-type tf_efficientnet_b5_ns --n-epochs 20 --stop-at-epoch 1 --fold 2 --load-from ${model_dir}/b5ns_DDP_final_576_300w_f2_40ep_fold2.pth

### B5

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b5ns_DDP_final_576_300w_f2_40ep --train-step 1 --data-dir ${data_dir} --image-size 576 --batch-size 24 --enet-type tf_efficientnet_b5_ns --n-epochs 40 --stop-at-epoch 33 --fold 2 --load-from ${model_dir}/b5ns_DDP_final_256_300w_f2_10ep_fold2.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b5ns_final_768_300w_f2_load33_5ep_3e-5_32G --train-step 2 --data-dir ${data_dir} --init-lr 3e-5 --image-size 768 --batch-size 13 --enet-type tf_efficientnet_b5_ns --n-epochs 5 --stop-at-epoch 4 --fold 2 --load-from ${model_dir}/b5ns_DDP_final_576_300w_f2_40ep_fold2.pth

### B4

python -u -m torch.distributed.launch --nproc_per_node=2 train.py --kernel-type b4ns_final_256_400w_f0_10ep --train-step 0 --data-dir ${data_dir} --image-size 256 --batch-size 128 --enet-type tf_efficientnet_b4_ns --n-epochs 10 --fold 0 --CUDA_VISIBLE_DEVICES 0,1

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b4ns_DDP_final_704_300w_f0_50ep --train-step 1 --data-dir ${data_dir} --image-size 704 --batch-size 22 --enet-type tf_efficientnet_b4_ns --n-epochs 50 --stop-at-epoch 16 --fold 0 --load-from ${model_dir}/b4ns_final_256_400w_f0_10ep_fold0.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b4ns_final_768_300w_f0_load16_20ep --train-step 2 --data-dir ${data_dir} --init-lr 5e-5 --image-size 768 --batch-size 16 --enet-type tf_efficientnet_b4_ns --n-epochs 20 --stop-at-epoch 1 --fold 0 --load-from ${model_dir}/b4ns_DDP_final_704_300w_f0_50ep_fold0.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b4ns_final_768_300w_f0_load16_20ep --train-step 3 --data-dir ${data_dir} --image-size 768 --batch-size 16 --enet-type tf_efficientnet_b4_ns --n-epochs 20 --stop-at-epoch 4 --fold 0 --load-from ${model_dir}/b4ns_final_768_300w_f0_load16_20ep_fold0.pth

### B3

python -u -m torch.distributed.launch --nproc_per_node=2 train.py --kernel-type b3ns_final_256_400w_f1_10ep --train-step 0 --data-dir ${data_dir} --image-size 256 --batch-size 128 --enet-type tf_efficientnet_b3_ns --n-epochs 10 --fold 1 --CUDA_VISIBLE_DEVICES 0,1

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b3ns_DDP_final_544_300w_f1_40ep --train-step 1 --data-dir ${data_dir} --image-size 544 --batch-size 17 --enet-type tf_efficientnet_b3_ns --n-epochs 40 --stop-at-epoch 29 --fold 1 --load-from ${model_dir}/b3ns_final_256_400w_f1_10ep_fold1.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b3ns_final_768_300w_f1_load29_5ep --train-step 2 --data-dir ${data_dir} --init-lr 5e-5 --image-size 768 --batch-size 22 --enet-type tf_efficientnet_b3_ns --n-epochs 5 --fold 1 --load-from ${model_dir}/b3ns_DDP_final_544_300w_f1_40ep_fold1.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type b3ns_final_768_300w_f1_load29_5ep5ep --train-step 3 --data-dir ${data_dir} --init-lr 5e-5 --image-size 768 --batch-size 22 --enet-type tf_efficientnet_b3_ns --n-epochs 5 --fold 1 --load-from ${model_dir}/b3ns_final_768_300w_f1_load29_5ep_fold1.pth

### ResNeSt-101

python -u -m torch.distributed.launch --nproc_per_node=6 train.py --kernel-type nest101_DDP_final_256_300w_f4_10ep_3e-5 --train-step 0 --data-dir ${data_dir} --init-lr 3e-5 --image-size 256 --batch-size 42 --enet-type nest101 --n-epochs 10 --fold 4 --CUDA_VISIBLE_DEVICES 0,1,2,3,4,5

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type nest101_DDP_final_576_300w_f4_40ep --train-step 1 --data-dir ${data_dir} --init-lr 3e-5 --image-size 576 --batch-size 30 --enet-type nest101 --n-epochs 40 --stop-at-epoch 16 --fold 4 --load-from ${model_dir}/nest101_DDP_final_256_300w_f4_10ep_3e-5_fold4.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type nest101_final_768_300w_f4_load16_ep20 --train-step 2 --data-dir ${data_dir} --init-lr 2e-5 --image-size 768 --batch-size 16 --enet-type nest101 --n-epochs 20 --stop-at-epoch 1 --fold 4 --load-from ${model_dir}/nest101_DDP_final_576_300w_f4_40ep_fold4.pth

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type nest101_final_768_300w_f4_load16_19ep_load1_16ep --train-step 3 --data-dir ${data_dir} --init-lr 4e-5 --image-size 768 --batch-size 16 --enet-type nest101 --n-epochs 16 --stop-at-epoch 5 --fold 4 --load-from ${model_dir}/nest101_final_768_300w_f4_load16_ep20_fold4.pth

### ReXNet 2.0

python -u -m torch.distributed.launch --nproc_per_node=4 train.py --kernel-type rex20_final_256_400w_f4_10ep --train-step 0 --data-dir ${data_dir} --image-size 256 --batch-size 64 --enet-type rex20 --n-epochs 10 --fold 4 --CUDA_VISIBLE_DEVICES 0,1,2,3

python -u -m torch.distributed.launch --nproc_per_node=8 train.py --kernel-type rex20_DDP_final_768_300w_f4_50ep --train-step 1 --data-dir ${data_dir} --image-size 768 --batch-size 19 --enet-type rex20 --n-epochs 50 --stop-at-epoch 38 --fold 4 --load-from ${model_dir}/rex20_DDP_final_768_300w_f4_50ep_fold4.pth

Predicting

This competition was a code competition. Teams submitted inference notebooks which were ran on hidden test sets. We made public the submission notebook on Kaggle at https://www.kaggle.com/boliu0/landmark-recognition-2020-third-place-submission

All the trained models are linked in that notebook as public datasets. The same notebook is also included in this repo for reference.

More Repositories

1

SIIM-ISIC-Melanoma-Classification-1st-Place-Solution

Python
195
star
2

MFNet-pytorch

MFNet-pytorch, image semantic segmentation using RGB-Thermal images
Python
82
star
3

python-IB-Trader

a minimal automated trader for American stock using official Interactive Brokers python API (ibapi)
Python
23
star
4

FC-DenseNet-Tiramisu-chainer

chainer implementation of FC-DenseNet (also named Tiramisu)
Python
3
star