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Neural Priming

Pytorch implementation of Neural Priming for Sample-Efficient Adaptation. Neural Priming improves transfer learning accuracy and robustness by recalling relevant data from the pretraining dataset.

Getting Started

If you'd like a demo of the code see the collab notebook.


  1. Clone this repository to your local machine using the following command:
git clone
  1. Navigate to the project directory:
cd neural-priming
  1. (Optional) - Create a conda environment:
conda create -n neural-priming python=3.8
conda activate neural-priming
  1. Install the required dependencies from the requirements.txt file:
pip install -r requirements.txt

Downloading The Data

Priming Data (LAION-2B)

To get started quickly we provide the priming subsets of LAION-2B for each target task. The link to download the data from Google Drive can be found here. If downloading to a headless server we recommend using gdown. Once downloaded, unzip and place in the /data folder in the root directory.

Alternatively, we provide code in the Text Filtering and Downloading Data section for creating your own priming subset.

Evaluation Data

  • To download ImageNet-1k: Download from this Kaggle link. The validation and train set should be in ImageFolder format which looks like the following.
├── ImageNet_Train
    ├── n01440764
        ├── img_1.jpeg
        ├── img_N.jpeg


    ├── n01632777
        ├── img_1.jpeg
        ├── img_N.jpeg
  • Torchvision Datasets: The 6 transfer learning datasets (StanfordCars, FGVC Aircraft, Flowers102, OxfordPets, and SUN397) will automatically be downloaded upon running the training code.

  • The other datasets (ImageNetV2, ImageNet-a, r, and sketch) can be found on their respective webpages. For ImageNetV2 we use the ImageNetV2-matched-frequency version.

Train and Evaluate Models

Zero-shot Priming

Example commands for priming and evaluating the model:

  • python Priming/ --dataset Flowers102 --shots 0 --alpha .7 --prime --subset_path ./data/Flowers102 --retrain
  • python Priming/ --dataset StanfordCars --shots 0 --prime --subset_path ./data/StanfordCars --retrain

Note: At this current time, StanfordCars is not available through torchvision. The dataset can be downloaded from kaggle.

  • python Priming/ --dataset ImageNet --shots 0 --prime --cupl --subset_path /data/ImageNet_subset --train_path ./data/ImageNet/train --val_path /data/ImageNet/val --retrain

To run the equivalent baselines, omit the --prime flag. For example:

python priming/ --dataset Flowers102 --shots 0 --subset_path ./data/Flowers102 --retrain

Zero-shot Results:

ImageNet Stanford Cars FGVC Aircraft Flowers102 Food101 Oxford Pets SUN397
CLIP Baseline 68.30 87.40 25.86 71.65 86.58 90.21 67.35
CuPL 70.25 88.63 29.64 72.32 86.20 91.16 70.80
Priming (Ours) 70.75 89.30 33.03 79.81 86.66 91.87 71.21
Priming + CuPL (Ours) 71.38 90.23 36.00 80.04 86.86 91.85 72.35

Few-shot Priming

Example commands for reproducing few-shot results. Note that alpha depends on the number of shots used.

python priming/ --dataset Flowers102 --shots 2 --alpha .58 --prime --subset_path ./data/Flowers102 --retrain
python priming/ --dataset FGVCAircraft --shots 3 --alpha .55 --prime --subset_path ./data/FGVCAircraft --retrain

Transductive Priming

Example commands for reproducing transductive results on the distribution shift datasets:

python --dataset ImageNet-V2 --shots 0 --prime --cupl  --subset_path ./data/ImageNetv2 --val_path ./data/ImageNetV2-matched-frequency --custom_data --retrain
ImageNet-V2 ImageNet-R ImageNet Sketch ImageNet-A
CLIP 62.61 64.57 57.05 35.95
Transduct. Priming (Ours) 64.23 79.37 59.97 38.20

Command line options:

  • --prime Use the priming subset to condition the model.
  • --retrain Reprocess the image features from the train/val/subset datasets. If already cached, omit to avoid reprocessing.
  • --text Initialize the classifier with the text prompts from OpenAI for ensembling with image features.
  • --cupl Initialize the classifier with text prompts from CuPL and OpenAI.
  • --cache Whether to cache the image features of the train/test/priming subset. Set to true by default. Set to false if low on disk space.
  • --alpha Ensembling coefficient between text and image features. Depends on the size of the training/priming set.
  • --shots Number of examples to be used from the target training set (not to be confused with the priming subset).
  • --model Change the base model. The priming subsets provided above are from the B-16 model.
  • --subset_path Path to the priming subset.
  • --val_path Path to the evaluation dataset. Only required for ImageNet and the distribution shift datasets.

For full list of command line options see

Creating Subsets from LAION-2B

Text filtering and Downloading Images

To quickly filter through the LAION-2B dataset using text, we use SQLite in python. The data base parquets can be downloaded here. We recommend gdown if downloading to a headless server. Once downloaded, place them in a /parquets folder. Each parquet is around 8 GB and all parquets are about 1 TB. If disk space is limited, you can download fewer parquets and filter on a subset of LAION-2B. Also note that placing the parquets on SSD will significantly improve search speed. You'll need the sqlite package which can be installed with pip install pysqlite3. Given the class names for a dataset, the code will filter for LAION-2B entries which have captions containing the class name and write the meta data to a json. Example to command to filter for ImageNet classes:

python ./DataFiltering/ -o ./ImageNet_Filtered -q ImageNet \
 -d  /parquets/part-00{000..123}-5114fd87-297e-42b0-9d11-50f1df323dfa-c000.snappy.db --template

Adjust the line text {000..123} if using fewer parquets. To filter using with respect to your own custom dataset, places the class names in a .py file in templates and set it as the --q argument in the above command.

Once the data is stored in the json, you can download the data from URLS using the following command:

python DataFiltering/ --r ./DataFiltering/ImageNet_Filtered/ --w ./data/ImageNet_filtered

Note that links break over time. A signficantly smaller number of images may be actually downloaded.

Transductive Filtering

Once the Text Filtering and Downloading Images has been completed, transductive filtering can be performed on the text-filtered subset. An example command would be the following:

Example command. Given the priming pool ImageNet_filtered and a path to a ground-truth dataset (ImageNet), this takes 10 dataset train images per class (--k-shot=10) and retrieves the 10 closest images for each of these from the priming pool (--retrievals-per-image=10):

python ./DataFilering/ \
        --dataset-type ImageNet \
        --retrieval-path "./data/ImageNet_filtered" \
        --transductive-path="/usr/data" \
        --cache-path="./data/kshot_cache_ImageNet" \
        --out-dir="/ImageNet" \
        --retrievals-per-image=10 \
        --prompt-file=./data/ImageNet.json \
        --k-shot=10 \

This returns a dataset in the ImageFolder format, where each retrieved image is labeled by its given class in the priming pool. If you use --split="test", this becomes the transductive setting discussed in the paper. You can add the --clip-filter command to apply a CLIP classifier to this new pool to further refine the dataset.

See the DataFiltering/ file for more details. Below is a list of arguments and descriptions:

usage: [-h] --retrieval-path RETRIEVAL_PATH --transductive-path TRANSDUCTIVE_PATH [--k-shot K_SHOT]
                     [--cache-path CACHE_PATH] --out-dir OUT_DIR [--retrievals-per-image RETRIEVALS_PER_IMAGE] [--clip-filter]
                     [--prompt-file PROMPT_FILE] [--dataset-type DATASET_TYPE] [--clip-score-filter CLIP_SCORE_FILTER]
                     [--split SPLIT] [--model MODEL] [--pretrained PRETRAINED]

optional arguments:
  -h, --help            show this help message and exit
  --retrieval-path RETRIEVAL_PATH
                        Path to retrieval reservoir (clean subset of LAION)
  --transductive-path TRANSDUCTIVE_PATH
                        Path to transfer evaluation dataset (to be used in a transductive fashion)
  --k-shot K_SHOT       Number of shots per class, only used in train-time data augmentation
  --cache-path CACHE_PATH
                        Path to cache
  --out-dir OUT_DIR     Path to output directory (dataset in ImageFolder format)
  --retrievals-per-image RETRIEVALS_PER_IMAGE
                        Number of retrievals per image
  --clip-filter         Filter using CLIP before transductive retrieval
  --prompt-file PROMPT_FILE
                        Path to prompt file, format classname to list of prompts
  --dataset-type DATASET_TYPE
                        Type of dataset
  --clip-score-filter CLIP_SCORE_FILTER
                        Filter using CLIP score, after clip classification filtering
  --split SPLIT         Split to use, only applies to non-ImageFolder datasets
  --model MODEL         Model arch from open_clip to use for filtering
  --pretrained PRETRAINED
                        Pre-trained weights from open_clip to use for filtering. See open_clip repo for choices


Code repository for the paper - "Neural Priming for Sample-Efficient Adaptation"







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