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oonat/
ezpc-checkpoints

Name: ezpc-checkpoints
Availability: InStock

pytorch

clip

siglip

zero-shot-image-classification

oonat/ezpc-embeddings

2603.28211

google/siglip-so400m-patch14-384

finetune

mit

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EZPC - Pre-trained Concept Projection Matrices

This repository hosts the trained projection matrices A for Explaining CLIP Zero-shot Predictions Through Concepts (CVPR 2026).

📄 Paper: arXiv:2603.28211
💻 Code: github.com/oonat/ezpc
🌐 Project page: oonat.github.io/ezpc
🤗 Embeddings: oonat/ezpc-embeddings

Repository Layout

Each checkpoint is a single PyTorch tensor file (best_A.pth) inside a folder whose name encodes the training configuration:

checkpoints/
└── <dataset>_backbone_<backbone>_weight_<λ>_epoch_<E>_lr_<lr>_bs_<bs>/
    └── best_A.pth

For example:

checkpoints/CIFAR-100_backbone_RN50_weight_1.0_epoch_10000_lr_0.01_bs_1000000/best_A.pth

The tensor best_A.pth has shape (d, m) where d is the backbone embedding dimension and m is the number of concepts in the dataset's concept vocabulary.

Quickstart

1. Download a checkpoint

bash

pip install huggingface-hub

# Download all checkpoints
hf download oonat/ezpc-checkpoints \
    --local-dir . \
    --include "checkpoints/*"

# Or just one
hf download oonat/ezpc-checkpoints \
    --local-dir . \
    --include "checkpoints/CIFAR-100_backbone_RN50_weight_1.0_epoch_10000_lr_0.01_bs_1000000/*"

2. Load and use it

The checkpoint can be loaded directly with PyTorch:

python

import torch

A = torch.load(
    "checkpoints/CIFAR-100_backbone_RN50_weight_1.0_epoch_10000_lr_0.01_bs_1000000/best_A.pth",
    weights_only=True,
).float()
print(A.shape)  # (d, m)

To run zero-shot evaluation, qualitative concept visualizations, faithfulness analyses, or any of the experiments from the paper, clone the EZPC GitHub repo.

Evaluation also requires the pre-computed image and cached text embeddings. Download them from the embeddings dataset into ./data:

hf download oonat/ezpc-embeddings --repo-type dataset --local-dir data

Then point --checkpoint_path at the downloaded best_A.pth:

bash

python test.py \
    --dataset CIFAR-100 \
    --dataset_root ./data \
    --backbone RN50 \
    --checkpoint_path ./checkpoints/CIFAR-100_backbone_RN50_weight_1.0_epoch_10000_lr_0.01_bs_1000000/best_A.pth

Citation

bibtex

@InProceedings{Ozdemir_2026_CVPR,
    author    = {Ozdemir, Onat and Christensen, Anders and Alaniz, Stephan and Akata, Zeynep and Akbas, Emre},
    title     = {Explaining CLIP Zero-shot Predictions Through Concepts},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2026},
    pages     = {31336-31345}
}

Acknowledgements

The concept vocabularies and class label mapping files were originally curated by the Label-free Concept Bottleneck Models authors. We thank them for open-sourcing these resources.

License

Released under the MIT License.

Note that these checkpoints were trained on embeddings derived from CIFAR-100, CUB-200-2011, Places365, ImageNet, and ImageNet-100. Users are responsible for complying with the original license and terms of use of those datasets, which may restrict commercial use — notably ImageNet and CUB-200-2011, which are released for non-commercial research only.