Sentinel-1 vessel detection model used in the xView3 challenge

Last update: Sep 10, 2022

Related tags

Overview

sar_vessel_detect

Code for the AI2 Skylight team's submission in the xView3 competition (https://iuu.xview.us) for vessel detection in Sentinel-1 SAR images. See whitepaper.pdf for a summary of our approach.

Dependencies

Install dependiences using conda:

cd sar_vessel_detect/
conda env create -f environment.yml

Pre-processing

First, ensure that training and validation scenes are extracted to the same directory, e.g. /xview3/all/images/. The training and validation labels should be concatenated and written to a CSV file like /xview3/all/labels.csv.

Prior to training, the large scenes must be split up into 800x800 windows (chips). Set paths and parameters in data/configs/chipping_config.txt, and then run:

cd sar_vessel_detect/src/
python -m xview3.processing.preprocessing ../data/configs/chipping_config.txt

Initial Training

We first train a model on the 50 xView3-Validation scenes only. We will apply this model in the xView3-Train scenes, and incorporate high-confidence predictions as additional labels. This is because xView3-Train scenes are not comprehensively labeled since most labels are derived automatically from AIS tracks.

To train, set paths and parameters in data/configs/initial.txt, and then run:

python -m xview3.training.train ../data/configs/initial.txt

Apply the trained model in xView3-Train, and incorporate high-confidence predictions as additional labels:

python -m xview3.infer.inference --image_folder /xview3/all/images/ --weights ../data/models/initial/best.pth --output out.csv --config_path ../data/configs/initial.txt --padding 400 --window_size 3072 --overlap 20 --scene_path ../data/splits/xview-train.txt
python -m xview3.eval.prune --in_path out.csv --out_path out-conf80.csv --conf 0.8
python -m xview3.misc.pred2label out-conf80.csv /xview3/all/chips/ out-conf80-tolabel.csv
python -m xview3.misc.pred2label_concat /xview3/all/chips/chip_annotations.csv out-conf80-tolabel.csv out-conf80-tolabel-concat.csv
python -m xview3.eval.prune --in_path out-conf80-tolabel-concat.csv --out_path out-conf80-tolabel-concat-prune.csv --nms 10
python -m xview3.misc.pred2label_fixlow out-conf80-tolabel-concat-prune.csv
python -m xview3.misc.pred2label_drop out-conf80-tolabel-concat-prune.csv out.csv out-conf80-tolabel-concat-prune-drop.csv
mv out-conf80-tolabel-concat-prune-drop.csv ../data/xval1b-conf80-concat-prune-drop.csv

Final Training

Now we can train the final object detection model. Set paths and parameters in data/configs/final.txt, and then run:

python -m xview3.training.train ../data/configs/final.txt

Attribute Prediction

We use a separate model to predict is_vessel, is_fishing, and vessel length.

python -m xview3.postprocess.v2.make_csv /xview3/all/chips/chip_annotations.csv out.csv ../data/splits/our-train.txt /xview3/postprocess/labels.csv
python -m xview3.postprocess.v2.get_boxes /xview3/postprocess/labels.csv /xview3/all/chips/ /xview3/postprocess/boxes/
python -m xview3.postprocess.v2.train /xview3/postprocess/model.pth /xview3/postprocess/labels.csv /xview3/postprocess/boxes/

Inference

Suppose that test images are in a directory like /xview3/test/images/. First, apply the object detector:

python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20
python -m xview3.eval.prune --in_path out.csv --out_path out-prune.csv --nms 10

Now apply the attribute prediction model:

python -m xview3.postprocess.v2.infer /xview3/postprocess/model.pth out-prune.csv /xview3/test/chips/ out-prune-attribute.csv attribute

Test-time Augmentation

We employ test-time augmentation in our final submission, which we find provides a small 0.5% performance improvement.

python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-1.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-2.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20 --fliplr True
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-3.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20 --flipud True
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-4.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20 --fliplr True --flipud True
python -m xview3.eval.ensemble out-1.csv out-2.csv out-3.csv out-4.csv out-tta.csv
python -m xview3.eval.prune --in_path out-tta.csv --out_path out-tta-prune.csv --nms 10
python -m xview3.postprocess.v2.infer /xview3/postprocess/model.pth out-tta-prune.csv /xview3/test/chips/ out-tta-prune-attribute.csv attribute

Confidence Threshold

We tune the confidence threshold on the validation set. Repeat the inference steps with test-time augmentation on the our-validation.txt split to get out-validation-tta-prune-attribute.csv. Then:

python -m xview3.eval.metric --label_file /xview3/all/chips/chip_annotations.csv --scene_path ../data/splits/our-validation.txt --costly_dist --drop_low_detect --inference_file out-validation-tta-prune-attribute.csv --threshold -1
python -m xview3.eval.prune --in_path out-tta-prune-attribute.csv --out_path submit.csv --conf 0.3 # Change to the best confidence threshold.

Inquiries

For inquiries, please open a Github issue.

Sentinel-1 vessel detection model used in the xView3 challenge

Related tags

Overview

sar_vessel_detect

Dependencies

Pre-processing

Initial Training

Final Training

Attribute Prediction

Inference

Test-time Augmentation

Confidence Threshold

Inquiries

Owner

AI2

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut

BoxInst: High-Performance Instance Segmentation with Box Annotations

Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻‍🦸🏽

Zsseg.baseline - Zero-Shot Semantic Segmentation

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

Lucid Sonic Dreams syncs GAN-generated visuals to music.

GULAG: GUessing LAnGuages with neural networks

Interactive Image Generation via Generative Adversarial Networks

Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper

Registration Loss Learning for Deep Probabilistic Point Set Registration

Code accompanying our NeurIPS 2021 traffic4cast challenge

Satellite labelling tool for manual labelling of storm top features such as overshooting tops, above-anvil plumes, cold U/Vs, rings etc.

Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models.

You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

This initial strategy was developed specifically for larger pools and is based on taking a moving average and deriving Bollinger Bands to create a projected active liquidity range.

Do you like Quick, Draw? Well what if you could train/predict doodles drawn inside Streamlit? Also draws lines, circles and boxes over background images for annotation.

In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

Classification of EEG data using Deep Learning

⚓ Eurybia monitor model drift over time and securize model deployment with data validation