Image Feature Extraction
Transformers
Safetensors
dreamsim
feature-extraction
perceptual-similarity
custom_code
Instructions to use bigshanedogg/dreamsim-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/dreamsim-ensemble with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="bigshanedogg/dreamsim-ensemble", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bigshanedogg/dreamsim-ensemble", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,600 Bytes
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Copyright (c) 2026 bigshanedogg. MIT License (see LICENSE).
This is a DERIVATIVE, re-packaged distribution and is NOT an official release by the
DreamSim authors.
Attribution and third-party notices
------------------------------------
1. DreamSim (MIT)
Copyright (c) 2023 Shobhita Sundaram, Netanel Tamir, Stephanie Fu, Richard Zhang
https://github.com/ssundaram21/dreamsim
The architecture, ensemble configuration, and released checkpoints originate here.
The weights in `model.safetensors` are the DreamSim "ensemble" LoRA adapters merged
into their base backbones.
2. DINO — Apache License 2.0 (see LICENSE.apache-2.0.txt)
Copyright (c) Facebook, Inc. and its affiliates.
https://github.com/facebookresearch/dino
The ViT backbone code in `modeling_dreamsim.py` is adapted from DINO's
`vision_transformer.py`. MODIFICATIONS: vendored into a single module, trimmed to
the inference path, restructured as a `transformers.PreTrainedModel`, and wired to
load merged safetensors weights. Per Apache-2.0 §4(b), these files carry notices
that they were changed.
3. Backbone pretraining weights (merged into `model.safetensors`)
* DINO ViT-B/16 — Apache-2.0 (Meta AI)
* CLIP ViT-B/16 — MIT (OpenAI, https://github.com/openai/CLIP)
* OpenCLIP ViT-B/16 — MIT code (https://github.com/mlfoundations/open_clip);
pretraining data is LAION-based.
Review these upstream terms — and, for OpenCLIP, the LAION data provenance —
before any commercial use.
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