bandiang2/car-data

Scraping and predicting car info

car-data

This is a hobby project to predict various attributes of cars from photos.

Demo and blog post:

• Blog post.
• Gradio demo

Usage

First, you should:

• Compile the scraper with cargo build --release.
• Install the Python package with pip install -e ..

Scraping data

To run the scraper, run:

./target/release/car-data scrape-kbb /path/to/db.db /path/to/images

In the above command, /path/to/db.db is the path where the metadata will be saved. It is stored as a sqlite3 database. The /path/to/images directory will be used to dump raw images.

To deduplicate and downsample the downloaded images, run:

./target/release/car-data dedup-images \
    /path/to/db.db \
    /path/to/images \
    /path/to/dedup

From here on out, we will use the /path/to/dedup directory instead of /path/to/images, since the former directory contains all of the images we will actually use for training.

To export the resulting dataset as a .npz file to load in Python, run:

./target/release/car-data export-data \
    /path/to/db.db \
    /path/to/index.npz

Filtering the dataset

To filter the dataset, you will first want to compute feature vectors for the entire dataset. These will be exported as a directory full of npz files with shards of features. You can do this with the following command:

python3 -m car_data.scripts.clip_features \
    /path/to/dedup \
    /path/to/features

Once you have labeled some images for the filter, you can train it quickly like so:

python3 -m car_data.scripts.train_filter \
    --positive_dirs /path/to/positive_dir \
    --negative_dirs /path/to/negative_dir \
    --model_out /path/to/filter.pt

To filter the dataset .npz file using the filter, you can use this command:

python3 -m car_data.scripts.filter_index \
    --index /path/to/index.npz \
    --feature_dir /path/to/features \
    --classifier_path /path/to/filter.pt \
    --output_path /path/to/index_filtered.npz

Training a model

To train a MobileNetV2 with auxiliary losses:

python3 -m car_data.scripts.train \
    --index_path /path/to/index_filtered.npz \
    --image_dir /path/to/dedup \
    --save_dir /path/to/mobilenetv2_save_dir \
    --lr 1e-4 \
    --batch_size 64 \
    --eval_interval 1 \
    --use_data_aug \
    --model mobilenetv2

To finetune CLIP with auxiliary losses:

python3 -m car_data.scripts.train \
    --index_path /path/to/index_filtered.npz \
    --image_dir /path/to/dedup \
    --save_dir /path/to/clip_save_dir \
    --lr 1e-5 \
    --batch_size 64 \
    --microbatch 16 \
    --eval_interval 1 \
    --use_data_aug \
    --model clip