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
| DreamSim (HuggingFace format) β unofficial port | |
| 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 β 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 Β§4(b), these files carry notices | |
| that they were changed. | |
| 3. Backbone pretraining weights (merged into `model.safetensors`) | |
| * DINO ViT-B/16 β Apache-2.0 (Meta AI) | |
| * CLIP ViT-B/16 β MIT (OpenAI, https://github.com/openai/CLIP) | |
| * OpenCLIP ViT-B/16 β MIT code (https://github.com/mlfoundations/open_clip); | |
| pretraining data is LAION-based. | |
| Review these upstream terms β and, for OpenCLIP, the LAION data provenance β | |
| before any commercial use. | |