6th Place Solution to iNat Challenge 2021: 10,000 Species Recognition Challenge with iNaturalist Data
Prepare an environment with python=3.8, tensorflow=2.3.1
Dependencies can be installed using the following command:
pip install -r requirements.txtPlease refer to the iNaturalist 2021 Competition Github page for additional dataset details and download links.
Use the script dataset_tools/create_inat2021_tf_records.py to generate the TFRecords files.
To train a classifier use the script main.py. As long as our final submission has two training stages, you can use the script multi_stage_train.py:
python multi_stage_train.py --training_files=PATH_TO_BE_CONFIGURED/inat_train.record-?????-of-02240 \
--num_training_instances=2686843 \
--validation_files=PATH_TO_BE_CONFIGURED/inat_val.record-?????-of-00084 \
--num_validation_instances=100000 \
--num_classes=10000 \
--model_name=efficientnet-b3 \
--input_size=300 \
--input_size_stage3=432 \
--input_scale_mode=uint8 \
--batch_size=32 \
--lr_stage1=0.1 \
--lr_stage2=0.1 \
--lr_stage3=0.008 \
--momentum=0.9 \
--epochs_stage1=0 \
--epochs_stage2=20 \
--epochs_stage3=2 \
--unfreeze_layers=18 \
--label_smoothing=0.1 \
--randaug_num_layers=6 \
--randaug_magnitude=4 \
--model_dir=PATH_TO_BE_CONFIGURED \
--random_seed=42The parameters can also be passed using a config file:
python multi_stage_train.py --flagfile=configs/efficientnet_b3_final_submission_training.config \
--model_dir=PATH_TO_BE_CONFIGUREDFor more parameter information, please refer to multi_stage_train.py or main.py. See configs folder for some training configs examples.
To train geo prior model used on our final submission please see our TF implementation.
To create a submission for the competition use script predict_main.py:
python predict_main.py --test_files=PATH_TO_BE_CONFIGURED/inat_public_test.record-?????-of-00417 \
--num_classes=10000 \
--model_name=efficientnet-b3 \
--input_size=432 \
--input_scale_mode=uint8 \
--batch_size=8 \
--ckpt_dir=PATH_TO_BE_CONFIGURED/ \
--geo_prior_ckpt_dir=PATH_TO_BE_CONFIGURED/ \
--submission_file_path=PATH_TO_BE_CONFIGURED/final_submission.csv \
--use_ttaEfficientnet-B3 was trained on iNat2021 train set, inference using input of 432x432. Geo Prior model was trained using coordinates and date info from iNat2021 train set.
| Model name | Private Score |
|---|---|
| Efficientnet-B3 | 0.16756 |
| Efficientnet-B3 + Geo Prior | 0.10752 |
| Efficientnet-B3 + Geo Prior + TTA | 0.09894 |
If you have any questions, feel free to contact Fagner Cunha (e-mail: [email protected]) or Github issues.
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