Surgical Instruments LoRA Adapter
This repository contains a LoRA adapter for OpenCLIP vision inference.
Modellquelle
- Art
- LoRA
- Dateigröße
- 2,82 MB
- Basismodell
- laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K
- Version
- vadapter_model
- Aktualisiert
- 22. Okt. 2026
- Quelle
- Hugging Face
Quellenbeschreibung
PEFT LoRA Adapter
This repository contains a LoRA adapter for OpenCLIP vision inference.
- Base model:
open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K - Adapter target:
visual.transformer - Task:
image_embedding
Usage
import open_clip
import torch
from peft import PeftModel
MODEL_NAME = 'hf-hub:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K'
ADAPTER_REPO = 'mounkie/surgical_instruments-lora-adapter'
model, _, preprocess = open_clip.create_model_and_transforms(MODEL_NAME)
model.visual.transformer = PeftModel.from_pretrained(
model.visual.transformer,
ADAPTER_REPO,
)
model.eval()
# image = preprocess(pil_image).unsqueeze(0)
# with torch.no_grad():
# image_features = model.encode_image(image)
# image_features = image_features / image_features.norm(dim=-1, keepdim=True)
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 22. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 Artefaktadapter_model.safetensors
safetensors · 2,82 MB · SHA-256 6d009ee7694c…7109 · Hugging Face
HerunterladenQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- library_name: peft pipeline_tag: image-feature-extraction tags: - peft - lora - open-clip - image-feature-extraction base_model: laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K --- # PEFT LoRA Adapter This repository contains a LoRA adapter for OpenCLIP vision inference. - Base model: `open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K` - Adapter target: `visual.transformer` - Task: `image_embedding` ## Usage ```python import open_clip import torch from peft import PeftModel MODEL_NAME = 'hf-hub:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K' ADAPTER_REPO = 'mounkie/surgical_instruments-lora-adapter' model, _, preprocess = open_clip.create_model_and_transforms(MODEL_NAME) model.visual.transformer = PeftModel.from_pretrained( model.visual.transformer, ADAPTER_REPO, ) model.eval() # image = preprocess(pil_image).unsqueeze(0) # with torch.no_grad(): # image_features = model.encode_image(image) # image_features = image_features / image_features.norm(dim=-1, keepdim=True) ```
Source context: 5 downloads · 0 likes · Pipeline image-feature-extraction · Library peft · Repo mounkie/surgical_instruments-lora-adapter
