---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- vision
- perceiver
- adaptive-computation
license: mit
datasets:
- timm/imagenet-12k-wds
---

# AdaPerceiver (Logit + Feature Distilled from ViT-H CLIP)

This repository hosts the **logit + feature distilled AdaPerceiver model**, introduced in  
**“AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens”**.

📄 Paper: https://arxiv.org/abs/2511.18105  
📦 Code: https://github.com/pjajal/AdaPerceiver  
📚 Model Collection: https://huggingface.co/collections/pjajal/adaperceiver-v1  

This model is distilled from [ViT-H CLIP model](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in12k).

---

## Model Description

**AdaPerceiver** is a Perceiver-style transformer architecture designed for **runtime-adaptive computation**.  
A single trained model can dynamically trade off **accuracy and compute** by adjusting:

- the **number of latent tokens**,
- the **effective depth**, and
- the **embedding dimension**.

This specific checkpoint corresponds to the **logit + feature distilled AdaPerceiver model**, trained on **ImageNet-12K** using a ViT-H teacher. It exposes both:
- classification **logits**, and
- **feature representations**

---

## Training Details

- **Training Data:** ImageNet-12K
- **Training Objective:** Logit distillation + feature distillation
- **Teacher Model:** [ViT-H/14 CLIP model](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in12k).
- **Architecture:** Adaptive Perceiver with block-masked attention and Matryoshka FFNs
- **Adaptivity Axes:** Tokens, Depth, Width

For full training details, see Appendix D of the paper.

---

## How to Use

This model can be loaded using the AdaPerceiver Hub-compatible class.

```python
import torch
from hub.networks.adaperceiver_distill import DistillAdaPerceiver

model = DistillAdaPerceiver.from_pretrained("pjajal/adaperceiver-v1")

# forward(
#   x: input image tensor (B, C, H, W)
#   num_tokens: number of latent tokens to process (optional)
#   mat_dim: embedding dimension (optional)
#   depth: early-exit depth (optional)
#   token_grans: block-mask granularities (optional)
# )
out = model(
    torch.randn(1, 3, 224, 224),
    num_tokens=256,
    mat_dim=128,
    depth=12,
)

print(out.logits.shape, out.features.shape)
```

## Reference

If you use this models please cite the AdaPerceiver paper:

```bibtex
@article{jajal2025adaperceiver,
  title={AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens},
  author={Jajal, Purvish and Eliopoulos, Nick John and Chou, Benjamin Shiue-Hal and Thiruvathukal, George K and Lu, Yung-Hsiang and Davis, James C},
  journal={arXiv preprint arXiv:2511.18105},
  year={2025}
}
```