vit_base_patch16_224.bamboo

A ViT-B/16 pretrained on Bamboo-CLS, converted from the authors' release to native timm weights. No fine-tuning or weight rescaling was performed. The checkpoint wrapper and any module. prefix were removed. The original 115,217-class classifier is retained.

Model details

• Paper: Bamboo
• Parameters: 174,400,529
• Input: RGB, 224 x 224
• Classifier outputs: 115,217
• Feature dimension: 768
• Training data: Bamboo-CLS

Preprocessing

Uses the authors' benchmark configuration: bicubic resize of the shorter side to 224, then a 224 x 224 center crop (crop_pct=1.0).

• Mean: (0.48145466, 0.4578275, 0.40821073)
• Standard deviation: (0.26862954, 0.26130258, 0.27577711)

The ViT benchmark uses CLIP normalization; the ResNet benchmark uses ImageNet normalization. The authors' separate ViT classification demo uses a different recipe. This export follows the benchmark.

Usage

import timm
import torch
from PIL import Image

model = timm.create_model("hf-hub:timm/vit_base_patch16_224.bamboo", pretrained=True).eval()
transform = timm.data.create_transform(**timm.data.resolve_model_data_config(model), is_training=False)
image = Image.open("image.jpg").convert("RGB")
with torch.inference_mode():
    output = model(transform(image).unsqueeze(0))

probabilities, indices = output.softmax(dim=-1).topk(5)
labels = model.pretrained_cfg["label_names"]
descriptions = model.pretrained_cfg["label_descriptions"]
for index, probability in zip(indices[0].tolist(), probabilities[0].tolist()):
    label_id = labels[index]
    print(label_id, descriptions[label_id], probability)

Labels

config.json includes label_names (classifier index to stable Bamboo concept ID) and label_descriptions (concept ID to English names/synonyms). Indices are ordered numerically using the authors' trainid2nid.json, not the ordering of the broader Bamboo taxonomy. The original trainid2name.json and trainid2nid.json are included alongside the weights.

Names are not unique: distinct concepts can share an English name. Preserve concept IDs when interpreting predictions. Blank source names use their concept ID as a fallback. Fallback rows: 5831 (Q24760423).

Pass num_classes=0 to extract pooled 768-dimensional features, or set num_classes to replace the pretrained classifier for fine-tuning.

Source and licensing

Source URLs and SHA256 hashes are recorded in conversion.json. The authors' demo metadata declares CC-BY-4.0; the dataset access instructions state academic, non-commercial use. These are upstream statements, not a new license grant for the converted weights.

Citation

@misc{zhang2022bamboo,
  title={Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy},
  author={Yuanhan Zhang and Qinghong Sun and Yichun Zhou and Zexin He and Zhenfei Yin and
           Kun Wang and Lu Sheng and Yu Qiao and Jing Shao and Ziwei Liu},
  year={2022},
  eprint={2203.07845},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}