| --- |
| task_categories: |
| - image-segmentation |
| license: cc-by-nc-4.0 |
| library_name: mmsegmentation |
| --- |
| |
| # CorrCLIPv2: Pre-generated Masks and Prebuilt Memory Banks |
|
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| This repository holds the pre-generated region masks and the prebuilt memory banks used to reproduce **CorrCLIPv2**, the journal extension of [CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation](https://arxiv.org/abs/2411.10086). |
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| CorrCLIPv2 augments single-image open-vocabulary segmentation with cross-image semantic support retrieved from an offline memory bank built purely from unlabeled images — no annotation, no caption, no training. Downloading the artifacts here lets you skip both the mask generation and the bank construction step. |
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| For the code, installation instructions and evaluation scripts, see the GitHub repository: |
| [https://github.com/zdk258/CorrCLIP](https://github.com/zdk258/CorrCLIP) (the `CorrCLIPv2/` subdirectory). |
|
|
| ## Region masks |
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| [instance_mask.zip](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/instance_mask.zip?download=true) (110 M) — EntitySeg region masks for the ten standard benchmarks. Extract it into `data/`, which gives `data/instance_mask/{voc,context,coco,ade,ade847,city}/` as `instance_mask_path` expects. With these in place, set `mask_generator=None` and no mask generator needs to be installed. |
|
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| ## Prebuilt banks |
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| Two banks are provided: `coco`, built from COCO train2017 and used for the main results, and `monet`, built from MONET(Z-Image) samples — fully synthetic and disjoint from every evaluation set. |
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| One configuration needs exactly three files — a main store matching the CLIP scale, the DFN auxiliary store, and the FAISS index — so pick a row and download only those. CorrCLIPv2-B and -L share the auxiliary store and the index; the only difference between them is `metaclip_b` vs `metaclip_l`. |
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| | Configuration | Files to download | Total | |
| |---|---|---| |
| | **CorrCLIPv2-L** + `coco` (main results) | [coco_metaclip_l.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/coco_metaclip_l.pt?download=true) 4.2G · [coco_dfnclip_b.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/coco_dfnclip_b.pt?download=true) 2.8G · [coco_radiov3.faiss](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/vfm_embeddings/coco_radiov3.faiss?download=true) 190M | 7.2G | |
| | **CorrCLIPv2-B** + `coco` | [coco_metaclip_b.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/coco_metaclip_b.pt?download=true) 2.8G · [coco_dfnclip_b.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/coco_dfnclip_b.pt?download=true) 2.8G · [coco_radiov3.faiss](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/vfm_embeddings/coco_radiov3.faiss?download=true) 190M | 5.8G | |
| | **CorrCLIPv2-L** + `monet` (synthetic, disjoint) | [monet_metaclip_l.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/monet_metaclip_l.pt?download=true) 2.2G · [monet_dfnclip_b.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/monet_dfnclip_b.pt?download=true) 1.4G · [monet_radiov3.faiss](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/vfm_embeddings/monet_radiov3.faiss?download=true) 108M | 3.7G | |
| | **CorrCLIPv2-B** + `monet` | [monet_metaclip_b.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/monet_metaclip_b.pt?download=true) 1.4G · [monet_dfnclip_b.pt](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/clip_embeddings/monet_dfnclip_b.pt?download=true) 1.4G · [monet_radiov3.faiss](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/vfm_embeddings/monet_radiov3.faiss?download=true) 108M | 3.0G | |
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| Put the `.pt` files in `memory_bank/clip_embeddings/` and the `.faiss` file in `memory_bank/vfm_embeddings/`, keeping the filenames unchanged — the loader resolves each by name. Then set `_clip_scale` and `_mb_bank` in `configs/base_config.py` to match the row you chose and run as usual; `vfm_type` stays `radiov3`, the space these indexes were built in. For the no-DMCE ablation, set `use_aux_model=False` and skip the DFN store. |
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| Each `.pt` store is an `[N, D]` fp16 tensor loaded fully into RAM at startup; its row index is the region id, and the FAISS index (IDMap2) returns the same ids, so search results index the stores directly. |
|
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| ## Bank image lists |
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| The image lists behind the disjoint banks are released for auditing — one source image path per line, the exact set each bank was built from: [gpic](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/bank_images_gpic.txt?download=true) (6.2 M), [monet](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/bank_images_monet.txt?download=true) (2.8 M), [sa1b](https://huggingface.co/datasets/dk258/CorrCLIPv2/resolve/main/memory_bank/bank_images_sa1b.txt?download=true) (1.0 M). The `coco` bank needs no list: it is the full COCO train2017 image set. |
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| ## Source data |
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| The masks are derived from the public validation images of the corresponding benchmarks; the `coco` bank is derived from COCO train2017 and the `monet` bank from the MONET Z-Image synthetic subset. Only derived features are redistributed here, never the source images — the original datasets' own licenses and terms continue to apply to them. The disjoint-bank source images are available from [SA-1B](https://ai.meta.com/datasets/segment-anything/), [GPIC](https://huggingface.co/datasets/stanford-vision-lab/gpic), and [MONET](https://huggingface.co/datasets/jasperai/monet). |
|
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| ## Citation |
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| The CorrCLIPv2 journal extension is under review; please cite the conference paper for now: |
|
|
| ```bibtex |
| @article{zhang2024corrclip, |
| title={Corrclip: Reconstructing patch correlations in clip for open-vocabulary semantic segmentation}, |
| author={Zhang, Dengke and Liu, Fagui and Tang, Quan}, |
| journal={arXiv preprint arXiv:2411.10086}, |
| year={2024} |
| } |
| ``` |
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