| --- |
| library_name: timm |
| pipeline_tag: image-classification |
| tags: |
| - timm |
| - bamboo |
| - arxiv:2203.07845 |
| --- |
| |
| # vit_base_patch16_224.bamboo |
| |
| A ViT-B/16 pretrained on Bamboo-CLS, converted from the |
| [authors' release](https://github.com/ZhangYuanhan-AI/Bamboo) 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](https://arxiv.org/abs/2203.07845) |
| - 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](https://github.com/ZhangYuanhan-AI/Bamboo/blob/f6337a76b3e48a1c5eff2bbd5b9dd044e42f0654/Bamboo-Benchmark/configs/models_cfg/ViTB16_bamboo_v0_1.yaml): 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 |
|
|
| ```python |
| 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](https://huggingface.co/spaces/CVPR/Bamboo_ViT-B16_demo/blob/2bbf27dfb670d5e1b1d3f1e0b97596f95e4c62a0/README.md) declares CC-BY-4.0; |
| the [dataset access instructions](https://github.com/ZhangYuanhan-AI/Bamboo/tree/f6337a76b3e48a1c5eff2bbd5b9dd044e42f0654#downloads) state academic, |
| non-commercial use. These are upstream statements, not a new license grant for the converted weights. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
