--- pipeline_tag: image-classification license: apache-2.0 base_model: timm/nextvit_small.bd_in1k library_name: zeromodels tags: - keras - zeromodels - image-classification - nextvit - backbone - arxiv:2207.05501 - pytorch - jax - tf --- ## ***See [our collection](https://huggingface.co/collections/zeromodels/next-vit-6a8eaece11e6326749bc87e4) for all versions of Next-ViT.*** # Run Next-ViT with Keras 3: JAX, PyTorch, or TensorFlow [![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-Next--ViT-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-Next--ViT%20collection-yellow)](https://huggingface.co/collections/zeromodels/next-vit-6a8eaece11e6326749bc87e4) # zeromodels/nextvit_small_bd_in1k Paper: [Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios (arXiv:2207.05501)](https://arxiv.org/abs/2207.05501) · [HF Papers](https://huggingface.co/papers/2207.05501) 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](https://huggingface.co/timm/nextvit_small.bd_in1k). Pure-**Keras 3** conversion of [`timm/nextvit_small.bd_in1k`](https://huggingface.co/timm/nextvit_small.bd_in1k) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**. This is an **image-classification / backbone** checkpoint (`NextViTImageClassify` / `NextViTModel`). ## ✨ Quick start ```python import os os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow" from PIL import Image import numpy as np from zeromodels.models.nextvit import NextViTImageClassify, NextViTModel model = NextViTImageClassify.from_weights("zeromodels/nextvit_small_bd_in1k") backbone = NextViTModel.from_weights( "zeromodels/nextvit_small_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("zeromodels/")`: | Variant | Hub | |---|---| | `nextvit_base_bd_in1k` | [`zeromodels/nextvit_base_bd_in1k`](https://huggingface.co/zeromodels/nextvit_base_bd_in1k) | | `nextvit_base_bd_in1k_384` | [`zeromodels/nextvit_base_bd_in1k_384`](https://huggingface.co/zeromodels/nextvit_base_bd_in1k_384) | | `nextvit_base_bd_ssld_6m_in1k` | [`zeromodels/nextvit_base_bd_ssld_6m_in1k`](https://huggingface.co/zeromodels/nextvit_base_bd_ssld_6m_in1k) | | `nextvit_base_bd_ssld_6m_in1k_384` | [`zeromodels/nextvit_base_bd_ssld_6m_in1k_384`](https://huggingface.co/zeromodels/nextvit_base_bd_ssld_6m_in1k_384) | | `nextvit_large_bd_in1k` | [`zeromodels/nextvit_large_bd_in1k`](https://huggingface.co/zeromodels/nextvit_large_bd_in1k) | | `nextvit_large_bd_in1k_384` | [`zeromodels/nextvit_large_bd_in1k_384`](https://huggingface.co/zeromodels/nextvit_large_bd_in1k_384) | | `nextvit_large_bd_ssld_6m_in1k` | [`zeromodels/nextvit_large_bd_ssld_6m_in1k`](https://huggingface.co/zeromodels/nextvit_large_bd_ssld_6m_in1k) | | `nextvit_large_bd_ssld_6m_in1k_384` | [`zeromodels/nextvit_large_bd_ssld_6m_in1k_384`](https://huggingface.co/zeromodels/nextvit_large_bd_ssld_6m_in1k_384) | | `nextvit_small_bd_in1k` | [`zeromodels/nextvit_small_bd_in1k`](https://huggingface.co/zeromodels/nextvit_small_bd_in1k) | | `nextvit_small_bd_in1k_384` | [`zeromodels/nextvit_small_bd_in1k_384`](https://huggingface.co/zeromodels/nextvit_small_bd_in1k_384) | | `nextvit_small_bd_ssld_6m_in1k` | [`zeromodels/nextvit_small_bd_ssld_6m_in1k`](https://huggingface.co/zeromodels/nextvit_small_bd_ssld_6m_in1k) | | `nextvit_small_bd_ssld_6m_in1k_384` | [`zeromodels/nextvit_small_bd_ssld_6m_in1k_384`](https://huggingface.co/zeromodels/nextvit_small_bd_ssld_6m_in1k_384) | ## Tips - Set `KERAS_BACKEND` **before** importing Keras / zeromodels. - `NextViTImageClassify` returns class logits; `NextViTModel` returns features (`as_backbone=True` for multi-scale stages). - See [docs](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/). - Upstream / timm checkpoints: `NextViTImageClassify.from_weights("hf:timm/nextvit_small.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).