README.md

metadata

pipeline_tag: image-classification
license: apache-2.0
base_model: timm/nextvit_large.bd_in1k
library_name: kerasformers
tags:
  - keras
  - kerasformers
  - image-classification
  - nextvit
  - backbone
  - arxiv:2207.05501
  - pytorch
  - jax
  - tf

See our collection for all versions of Next-ViT.

Run Next-ViT with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/nextvit_large_bd_in1k

Paper: Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios (arXiv:2207.05501) · HF Papers

Next-ViT targets efficient industrial deployment with a hybrid CNN/Transformer stack. Classifier or hierarchical backbone.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of timm/nextvit_large.bd_in1k for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (NextViTImageClassify / NextViTModel).

✨ Quick start

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
import numpy as np
from kerasformers.models.nextvit import NextViTImageClassify, NextViTModel

model = NextViTImageClassify.from_weights("kerasformers/nextvit_large_bd_in1k")
backbone = NextViTModel.from_weights(
    "kerasformers/nextvit_large_bd_in1k", as_backbone=True
)

image = Image.open("your_image.jpg").convert("RGB")
image = image.resize((224, 224))
x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
print(model(x).shape)  # (1, num_classes)
feats = backbone(x)
print(len(feats), [tuple(f.shape) for f in feats])

Load any Next-ViT variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
nextvit_base_bd_in1k	kerasformers/nextvit_base_bd_in1k
nextvit_base_bd_in1k_384	kerasformers/nextvit_base_bd_in1k_384
nextvit_base_bd_ssld_6m_in1k	kerasformers/nextvit_base_bd_ssld_6m_in1k
nextvit_base_bd_ssld_6m_in1k_384	kerasformers/nextvit_base_bd_ssld_6m_in1k_384
nextvit_large_bd_in1k	kerasformers/nextvit_large_bd_in1k
nextvit_large_bd_in1k_384	kerasformers/nextvit_large_bd_in1k_384
nextvit_large_bd_ssld_6m_in1k	kerasformers/nextvit_large_bd_ssld_6m_in1k
nextvit_large_bd_ssld_6m_in1k_384	kerasformers/nextvit_large_bd_ssld_6m_in1k_384
nextvit_small_bd_in1k	kerasformers/nextvit_small_bd_in1k
nextvit_small_bd_in1k_384	kerasformers/nextvit_small_bd_in1k_384
nextvit_small_bd_ssld_6m_in1k	kerasformers/nextvit_small_bd_ssld_6m_in1k
nextvit_small_bd_ssld_6m_in1k_384	kerasformers/nextvit_small_bd_ssld_6m_in1k_384

Tips

• Set KERAS_BACKEND before importing Keras / kerasformers.
• NextViTImageClassify returns class logits; NextViTModel returns features (as_backbone=True for multi-scale stages).
• See docs and Loading Weights.
• Upstream / timm checkpoints: NextViTImageClassify.from_weights("hf:timm/nextvit_large.bd_in1k").

Special Thanks

A huge thank you to the Next-ViT authors and the timm / Hub communities for creating and releasing these models.

License: see YAML license (usually matches the upstream checkpoint).