Dreamsim

**We're releasing new DreamSim models that are compatible with updated versions of peft!**

If you're already using one of the DreamSim models, you just need to make the following changes:
* Update the `dreamsim` package to version `0.2.0`.
* Update your environment to use `peft >= 0.2.0`.
* Remove any old local/cached dreamsim checkpoints. The next time you call the main `dreamsim` function, it will automatically download the updated checkpoints.

Here's how the new models perform on NIGHTS:
| | NIGHTS - Val | NIGHTS - Test |
|-------------------|--------------|---------------|
| `ensemble` | 96.6% | 96.1% |
| `dino_vitb16` | 95.7% | 94.8% |
| `open_clip_vitb32` | 95.6% | 93.6% |
| `clip_vitb32`| 95.5% | 95.3% |

**We're also releasing updates/additions to the NIGHTS dataset:**
* If you're having trouble with large file sizes when downloading NIGHTS, you can now run `./dataset/download_chunked_dataset.sh` to get the dataset split into 200 smaller zips.
* We only use the 20k unanimous triplets for training and evaluation, but release all 100k triplets (many with few and/or split votes) for research purposes. Run `./dataset/download_unfiltered_dataset.sh` to download and unzip this unfiltered version of NIGHTS dataset (289 GB)
* Download the just-noticeable difference (JND) votes by running `./dataset/download_jnd_dataset.sh`. We've also updated the [DreamSim Colab](https://colab.research.google.com/drive/1taEOMzFE9g81D9AwH27Uhy2U82tQGAVI?usp=sharing) with an example of loading a JND trial.

v0.2.0-checkpoints
Checkpoints for v0.2.0 release.

* Fixed a bug with caching ensemble model checkpoints.

We're releasing three lighter-weight versions of DreamSim that each use only one ViT model (instead of the full ensemble). The backbone options are DINO-ViTB/16, CLIP-ViTB/32, and OpenCLIP-ViTB/32.

To load a single-backbone version of dreamsim, use the new `dreamsim_type` argument (defaults to "ensemble"). For example:

dreamsim_dino_model, preprocess = dreamsim(pretrained=True, dreamsim_type="dino_vitb16")

Here's how the single-backbone finetuned models compare to the ensemble on NIGHTS:
* **Ensemble**: 96.2%
* **OpenCLIP-ViTB/32**: 95.5%
* **DINO-ViTB/16**: 94.6%
* **CLIP-ViTB/32**: 93.9%

For more details please refer to our paper.