WingCLIP-0.1

An 86.6M-parameter bird image encoder distilled from BioCLIP-2 and then fine-tuned past it. 89.93 top-1 on NABirds against the teacher's 86.41, at 3.5x fewer visual parameters.

It is the most accurate model in the WingDex family. It is not the one WingDex ships: at 346 MB fp32 it does not fit the 25 MB-per-file web budget, so the app runs WingCLIP-0.3 instead.

WingCLIP-0.1's other job is to be the teacher of WingCLIP-0.3. You need this model to reproduce 0.3.

Lineage

	model	visual params	NABirds top-1
teacher	BioCLIP-2, ViT-L/14	304.0M	86.41
this model	WingCLIP-0.1, ViT-B-16	86.6M	89.93
student	WingCLIP-0.3, TinyCLIP-39M	38.7M	86.90

How a student beats its teacher

Distillation alone cannot exceed the teacher, because the teacher's embedding is the regression target. The distill stage lands at 81.83, well short of 86.41. The gain comes from the next stage: a supervised fine-tune on ground-truth bird photos, which adds label information BioCLIP-2 never saw. Retention above 100% is the expected outcome of that, not an anomaly.

What it is

A ViT-B-16 visual tower whose output is projected into the 768-d BioCLIP-2 embedding space and L2-normalized. forward() is the whole exportable graph: no text encoder runs at inference time.

Classification is a cosine similarity against a frozen 11,167 x 768 matrix of BioCLIP-2 text embeddings, shipped here as text_classifier_fp32.npy. It is byte-identical to the one in the 0.3 repo; both models target the same space.

Files

file	what
wingclip-0.1.pt	the released checkpoint, WiSE-FT alpha 0.90
wingclip-0.1.safetensors	the same weights, without the pickle
wingclip-0.1-alpha.pt	after distillation, before fine-tuning. NABirds 81.83
wingclip-0.1-beta.pt	after fine-tuning, before the WiSE-FT merge
text_classifier_fp32.npy	11,167 x 768 frozen BioCLIP-2 text embeddings
labels.json	11,167 rows of [common name, scientific name, eBird code], in classifier row order

No ONNX here. 0.1 is not deployed anywhere, and the exports on hand predate the released checkpoint by enough that I could not confirm they came from it. Use export_onnx.py in the WingDex repo if you need one.

Reconstructing the alpha sweep

The release is an exact linear interpolation of the two stages:

released = (1 - a) * alpha + a * beta        # a = 0.90, verified to 0.0e+00

So wingclip-0.1-alpha.pt and wingclip-0.1-beta.pt regenerate any point on the sweep. Measured NABirds top-1, for reference:

a	0.25	0.50	0.75	0.90	1.00
top-1	85.86	88.42	89.69	89.93	89.77

Usage

import json
import numpy as np
import open_clip
import torch
import torch.nn.functional as F
from huggingface_hub import hf_hub_download
from PIL import Image

REPO = "johnlian/WingCLIP-0.1"


class WingCLIP01(torch.nn.Module):
    def __init__(self):
        super().__init__()
        model, _, self.preprocess = open_clip.create_model_and_transforms(
            "ViT-B-16", pretrained=None)
        self.visual = model.visual
        self.proj = torch.nn.Linear(512, 768)

    def forward(self, x):
        return F.normalize(self.proj(self.visual(x)), dim=-1)


model = WingCLIP01().eval()
ckpt = torch.load(hf_hub_download(REPO, "wingclip-0.1.pt"), map_location="cpu")
model.load_state_dict(ckpt["model"])

classifier = np.load(hf_hub_download(REPO, "text_classifier_fp32.npy"))
labels = json.load(open(hf_hub_download(REPO, "labels.json")))

img = model.preprocess(Image.open("bird.jpg").convert("RGB")).unsqueeze(0)
with torch.no_grad():
    emb = model(img).numpy()

sims = (emb @ classifier.T)[0]
for i in sims.argsort()[-5:][::-1]:
    print(f"{sims[i]:.4f}  {labels[i][0]}  ({labels[i][1]})")

Preprocessing is standard CLIP at 224x224: mean (0.48145466, 0.4578275, 0.40821073), std (0.26862954, 0.26130258, 0.27577711).

The text embeddings were built with the prompt "a photo of {common name}, {scientific name}, a species of bird.", so keep that template if you rebuild the classifier.

How it was trained

1. Distil. A LAION ViT-B-16 visual tower regresses BioCLIP-2 embeddings over 2,503,107 iNaturalist photos across 7,555 species, 20 epochs at lr 1e-4. val cosine 0.9650, NABirds 81.83.
2. Fine-tune. 12 epochs of supervised fine-tuning on ground-truth photos, lr 1e-5. In-distribution GT-val 77.61.
3. Merge. WiSE-FT interpolation at alpha = 0.90, giving NABirds 89.93.

The optimum sits at 0.90 where the WiSE-FT paper finds roughly 0.5, because this fine-tune is gentle: it moves about 4.7% of the weights, mostly the projection and the last blocks. WingCLIP-0.3's stronger fine-tune moves its optimum down to 0.60.

Quantization

NABirds, all 24,633 images.

variant	~MB	top-1	delta
fp32	346	89.94	-
fp16	173	89.94	+0.00
int8	87	89.89	-0.05
int4 block 128	43	89.06	-0.88
int3 block 128	32	0.00	collapse

int3 does not degrade, it fails outright.

Limitations

• Too large for the web. 87 MB at int8 against a 25 MB-per-file cap is why WingCLIP-0.3 exists.
• Birds only. No notion of "not a bird". Feed it a dog and it returns a bird.
• Low confidence means species ambiguity, not bad framing. Top-1 confidence against relative bird area is Pearson 0.051.
• North-American evaluation. NABirds is the deciding benchmark, so accuracy elsewhere is less well characterised.
• Long tail. Of 11,167 predictable species, 7,555 were distilled on. The rest ride entirely on the text embedding of their name.

Licence and attribution

Weights are CC BY-NC 4.0. The WingDex source code is MIT, but the weights are trained on iNaturalist photos of which 1,923,704 are CC-BY-NC, so non-commercial propagates to the weights.

Training data: iNaturalist Open Data. 2,503,107 images, 7,555 species, 62,423 credited observers. ShareAlike-licensed photos were excluded.

Upstream: LAION CLIP ViT-B-16 supplied the initial weights; BioCLIP-2 (MIT) was the teacher and supplies the embedding space.

Citation

WingCLIP has no paper of its own; cite the repository and the work below. The full bibliography is on the WingCLIP-0.3 card; the two that matter most here are BioCLIP 2 and WiSE-FT.

@inproceedings{gu2025bioclip2,
  title     = {{BioCLIP} 2: Emergent Properties from Scaling Hierarchical Contrastive Learning},
  author    = {Gu, Jianyang and Stevens, Sam and Campolongo, Elizabeth and Thompson, Matthew and Zhang, Net and Wu, Jiaman and Kopanev, Andrei and Mai, Zheda and White, Alexander and Balhoff, James and Dahdul, Wasila and Rubenstein, Daniel and Lapp, Hilmar and Berger-Wolf, Tanya and Chao, Wei-Lun and Su, Yu},
  booktitle = {Advances in Neural Information Processing Systems},
  volume    = {38},
  pages     = {102778--102811},
  year      = {2025},
  eprint    = {2505.23883},
  archivePrefix = {arXiv}
}

@inproceedings{wortsman2022robust,
  title     = {Robust fine-tuning of zero-shot models},
  author    = {Wortsman, Mitchell and Ilharco, Gabriel and Kim, Jong Wook and Li, Mike and Kornblith, Simon and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Hajishirzi, Hannaneh and Farhadi, Ali and Namkoong, Hongseok and Schmidt, Ludwig},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022},
  eprint    = {2109.01903},
  archivePrefix = {arXiv}
}