ModelListadosExterno

Siglip2 Large LoRA v3

LoRA fine-tune of google/siglip2-large-patch16-384 for material/surface visual-similarity retrieval (room crop → catalog product), trained with Supervised Contrastive loss on a combined v1-mined + v3-synthetic dataset.

Fontehuggingface.co/subhrokomol/siglip2-large-lora-v3/tree/f607779abdcfa68b89b0059f3da3c53b9bbf7bbc

TipoOtherModelo basegoogle/siglip2-large-patch16-384VersãoRevision f607779Cnota

Fonte do modelo

Tipo: Other
Tamanho do arquivo: 12,2 MB
Modelo base: google/siglip2-large-patch16-384
Versão: vRevision f607779
Atualizado: 22 de out. de 2026
Downloads: 6
Fonte: Hugging Face

Descrição da fonte

siglip2-large-lora-v3

LoRA fine-tune of google/siglip2-large-patch16-384 for material/surface visual-similarity retrieval (room crop → catalog product), trained with Supervised Contrastive loss on a combined v1-mined + v3-synthetic dataset.

Retrieval eval (crop → full catalog gallery)

n	recall@1	recall@5	recall@10
845	0.303	0.5136	0.6154

Per-category / per-slot

stratum	n	r@1	r@5	r@10
slot:wall	111	0.0811	0.2793	0.3604
slot:floors	87	0.046	0.2184	0.3678
slot:ceiling	78	0.0641	0.2692	0.4231
cat:Fabric & Textile	47	0.6596	0.8936	0.9787
slot:table	42	0.0714	0.3333	0.4762
slot:rug	41	0.122	0.3902	0.6098
cat:Wallpaper & Wallcovering	36	0.6944	0.8611	0.9444
slot:cabinetry	36	0.0833	0.3333	0.5278
cat:Handle / Knob	35	0.4857	0.7714	0.8571
slot:sofa	34	0.1176	0.3824	0.4412
cat:Tile	33	0.697	0.8788	0.8788
slot:chair	28	0.0357	0.2857	0.3929
cat:Stone & Masonry	27	0.8148	1.0	1.0
cat:Flooring	26	0.7308	0.9615	1.0
cat:Paint	21	0.619	0.8095	0.8095
cat:Carpet	18	1.0	1.0	1.0
slot:cladding	17	0.0588	0.1765	0.2353
slot:worktop	16	0.0	0.25	0.5
cat:Wood	13	0.6154	0.9231	0.9231
cat:Engineered Surface	13	0.7692	0.9231	0.9231
slot:rod_bar	13	0.0769	0.3846	0.6154
slot:curtain	12	0.0833	0.3333	0.4167
cat:Glass	12	0.75	0.9167	1.0
cat:Laminate	10	0.1	0.3	0.5
cat:Leather	10	0.9	1.0	1.0
cat:Metal	9	0.3333	0.7778	1.0
cat:Faucet / Tap	8	0.75	1.0	1.0
cat:Plastic	6	0.8333	0.8333	0.8333
slot:moulding	6	0.0	0.0	0.0

Training

epochs run: 10
final train_loss: 0.0900
LoRA r=16, α=32, dropout=0.1; SupCon τ=0.07; batch 16; lr 1e-4 cosine

Load

from peft import PeftModel; from transformers import AutoModel
base = AutoModel.from_pretrained('google/siglip2-large-patch16-384')
model = PeftModel.from_pretrained(base, 'subhrokomol/siglip2-large-lora-v3')

Fontes

1 fonte

Hugging FacePrimárioVersão f607779abdcfa68b89b0059f3da3c53b9bbf7bbc

huggingface.co/subhrokomol/siglip2-large-lora-v3/tree/f607779abdcfa68b89b0059f3da3c53b9bbf7bbc

Verificado 22 de out.

Artefatos de modelo

1 artefato

adapter_model.safetensors

safetensors · 12,2 MB · SHA-256 436527073ce7…8edd · Hugging Face

Download file

Trechos de fonte

3 trechos

Readme5 de ago.huggingface.co/subhrokomol/siglip2-large-lora-v3

--- license: apache-2.0 base_model: google/siglip2-large-patch16-384 library_name: peft tags: - siglip - siglip2 - lora - peft - embedding - material-recognition - visual-similarity pipeline_tag: image-feature-extraction --- # siglip2-large-lora-v3 LoRA fine-tune of `google/siglip2-large-patch16-384` for material/surface visual-similarity retrieval (room crop → catalog product), trained with Supervised Contrastive loss on a combined v1-mined + v3-synthetic dataset. ## Retrieval eval (crop → full catalog gallery) | n | recall@1 | recall@5 | recall@10 | |--:|--:|--:|--:| | 845 | 0.303 | 0.5136 | 0.6154 | ### Per-category / per-slot | stratum | n | r@1 | r@5 | r@10 | |---|--:|--:|--:|--:| | slot:wall | 111 | 0.0811 | 0.2793 | 0.3604 | | slot:floors | 87 | 0.046 | 0.2184 | 0.3678 | | slot:ceiling | 78 | 0.0641 | 0.2692 | 0.4231 | | cat:Fabric & Textile | 47 | 0.6596 | 0.8936 | 0.9787 | | slot:table | 42 | 0.0714 | 0.3333 | 0.4762 | | slot:rug | 41 | 0.122 | 0.3902 | 0.6098 | | cat:Wallpaper & Wallcovering | 36 | 0.6944 | 0.8611 | 0.9444 | | slot:cabinetry | 36 | 0.0833 | 0.3333 | 0.5278 | | cat:Handle / Knob | 35 | 0.4857 | 0.7714 | 0.8571 | | slot:sofa | 34 | 0.1176 | 0.3824 | 0.4412 | | cat:Tile | 33 | 0.697 | 0.8788 | 0.8788 | | slot:chair | 28 | 0.0357 | 0.2857 | 0.3929 | | cat:Stone & Masonry | 27 | 0.8148 | 1.0 | 1.0 | | cat:Flooring | 26 | 0.7308 | 0.9615 | 1.0 | | cat:Paint | 21 | 0.619 | 0.8095 | 0.8095 | | cat:Carpet | 18 | 1.0 | 1.0 | 1.0 | | slot:cladding | 17 | 0.0588 | 0.1765 | 0.2353 | | slot:worktop | 16 | 0.0 | 0.25 | 0.5 | | cat:Wood | 13 | 0.6154 | 0.9231 | 0.9231 | | cat:Engineered Surface | 13 | 0.7692 | 0.9231 | 0.9231 | | slot:rod_bar | 13 | 0.0769 | 0.3846 | 0.6154 | | slot:curtain | 12 | 0.0833 | 0.3333 | 0.4167 | | cat:Glass | 12 | 0.75 | 0.9167 | 1.0 | | cat:Laminate | 10 | 0.1 | 0.3 | 0.5 | | cat:Leather | 10 | 0.9 | 1.0 | 1.0 | | cat:Metal | 9 | 0.3333 | 0.7778 | 1.0 | | cat:Faucet / Tap | 8 | 0.75 | 1.0 | 1.0 | | cat:Plastic | 6 | 0.8333 | 0.8333 | 0.8333 | | slot:moulding | 6 | 0.0 | 0.0 | 0.0 | ## Training - epochs run: 10 - final train_loss: 0.0900 - LoRA r=16, α=32, dropout=0.1; SupCon τ=0.07; batch 16; lr 1e-4 cosine ## Load ```python from peft import PeftModel; from transformers import AutoModel base = AutoModel.from_pretrained('google/siglip2-large-patch16-384') model = PeftModel.from_pretrained(base, 'subhrokomol/...

Note22 de out.huggingface.co/subhrokomol/siglip2-large-lora-v3

Source context: 6 downloads · 0 likes · Pipeline image-feature-extraction · Library peft · Repo subhrokomol/siglip2-large-lora-v3

Note5 de ago.huggingface.co/subhrokomol/siglip2-large-lora-v3

Source context: 1 downloads · 0 likes · Pipeline image-feature-extraction · Library peft · Repo subhrokomol/siglip2-large-lora-v3