KO
|
EN
gitlite — search
Search
#python
#java
#python3
#arduino
#golang
#machine-learning
#rust
#html
#flask
#javascript
#seismology
#nodejs
humpback-whale-4th-place
★ 27
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
SDLBook
:
simple djvu/pdf ebook viewer based on djvulibre, libmupdf and SDL 1.2/2
ml-retrospectives.github.io
:
No description available.
CCOnOff
:
Properly toggle Wi-Fi and Bluetooth from Control Center (iOS 11+).
selenium-openapi
:
The missing Selenium OpenAPI spec
MemoryScanner
:
Scan Like a Cheat Engine
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
humpback-whale-4th-place
?
Download (.md)
Below you can find a outline of how to reproduce my solution for the Humpback Whale Identification competition. #### HARDWARE REQUIREMENTS: (The following specs were used to create the original solution) Ubuntu 16.04 LTS 128GB RAM (64GB minimum required to run keypoint matching) Core i9-9980X CPU 8x GPU w/24GB memory each (required to run batch sizes in siamese network training) *note that even with the above HW, training may take 13-30+ days to complete due to exhaustive brute-force keypoint matching* #### SOFTWARE (python packages are detailed separately in `requirements.txt`): Python 3.5.1 CUDA 10.0 cuddn 7.3 #### DATA SETUP ``` mkdir -p whale/input cd whale/input ``` download the competition train/test files and folders into whale/input ``` unzip mask_predictions_test_4.zip unzip mask_predictions_train_known_4.zip ``` copy all images !=new_whale from train into the folder: train_specific copy all images from train into folder: val #### Keypoint matching: ``` cd kps python index_features_2kp.py --dataset ../input/train_specific --features-db train --mask-db ../input/mask_predictions_train_known_4 python index_features_2kp.py --dataset ../input/test --features-db test --mask-db ../input/mask_predictions_test_4 python kp_matching.py ``` #### Base model training note: for this step training is done with close attention to training/val loss and final checkpoints need to be selected at the ned of each training cycle and the filenames put back into the training files at the appropriate insertion points One set of train and inference files are included here. To replicate the solution results, please substiture DenseNet121, Resnet50, and InceptionV3 as the basenetworks and run each for both train and inference (6 total runs: 3 networks x (train + inference). ``` cd ../base_network python densenet121_200_train.py python densenet121_422_train.py ``` #### Siamese model training note: in this step as well, the output of the previous steps (which can't be predetermined due to checkpoint number and val_loss in file name) will need to be inserted into the py files before running One set of train and inference files are included here. To replicate the solution results, please substiture DenseNet121, Resnet50, and InceptionV3 as the basenetworks and run each for both train and inference (6 total runs: 3 networks x (train + inference). ``` cd ../siamese python siamese_train.py python siamese_inference.py ``` #### Ensemble note: in this step as well, the output of the previous steps ``` cd ../ensemble python ensemble.py ```
