README.md

---
pipeline_tag: image-classification
license: apache-2.0
base_model: timm/convnext_xlarge.fb_in22k
library_name: zeromodels
tags:
- keras
- zeromodels
- image-classification
- convnext
- backbone
- arxiv:2201.03545
- pytorch
- jax
- tf
---

## ***See [our collection](https://huggingface.co/collections/zeromodels/convnext-6a8eaf1426a17001e2adc554) for all versions of ConvNeXt.***

# Run ConvNeXt 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-ConvNeXt-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-ConvNeXt%20collection-yellow)](https://huggingface.co/collections/zeromodels/convnext-6a8eaf1426a17001e2adc554)

# zeromodels/convnext_xlarge_fb_in22k

Paper: [A ConvNet for the 2020s (arXiv:2201.03545)](https://arxiv.org/abs/2201.03545) · [HF Papers](https://huggingface.co/papers/2201.03545)

ConvNeXt modernizes a ResNet-style CNN with ViT-inspired design choices. Use as ImageNet classifier or 4-stage backbone.

For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/convnext_xlarge.fb_in22k).

Pure-**Keras 3** conversion of [`timm/convnext_xlarge.fb_in22k`](https://huggingface.co/timm/convnext_xlarge.fb_in22k) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.

This is an **image-classification / backbone** checkpoint (`ConvNeXtImageClassify` / `ConvNeXtModel`).

## ✨ Quick start

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

from PIL import Image
import numpy as np
from zeromodels.models.convnext import ConvNeXtImageClassify, ConvNeXtModel

model = ConvNeXtImageClassify.from_weights("zeromodels/convnext_xlarge_fb_in22k")
backbone = ConvNeXtModel.from_weights(
    "zeromodels/convnext_xlarge_fb_in22k", 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 ConvNeXt variant the same way with `from_weights("zeromodels/<variant>")`:

| Variant | Hub |
|---|---|
| `convnext_atto_d2_in1k` | [`zeromodels/convnext_atto_d2_in1k`](https://huggingface.co/zeromodels/convnext_atto_d2_in1k) |
| `convnext_base_fb_in1k` | [`zeromodels/convnext_base_fb_in1k`](https://huggingface.co/zeromodels/convnext_base_fb_in1k) |
| `convnext_base_fb_in22k` | [`zeromodels/convnext_base_fb_in22k`](https://huggingface.co/zeromodels/convnext_base_fb_in22k) |
| `convnext_base_fb_in22k_ft_in1k` | [`zeromodels/convnext_base_fb_in22k_ft_in1k`](https://huggingface.co/zeromodels/convnext_base_fb_in22k_ft_in1k) |
| `convnext_base_fb_in22k_ft_in1k_384` | [`zeromodels/convnext_base_fb_in22k_ft_in1k_384`](https://huggingface.co/zeromodels/convnext_base_fb_in22k_ft_in1k_384) |
| `convnext_femto_d1_in1k` | [`zeromodels/convnext_femto_d1_in1k`](https://huggingface.co/zeromodels/convnext_femto_d1_in1k) |
| `convnext_large_fb_in1k` | [`zeromodels/convnext_large_fb_in1k`](https://huggingface.co/zeromodels/convnext_large_fb_in1k) |
| `convnext_large_fb_in22k` | [`zeromodels/convnext_large_fb_in22k`](https://huggingface.co/zeromodels/convnext_large_fb_in22k) |
| `convnext_large_fb_in22k_ft_in1k` | [`zeromodels/convnext_large_fb_in22k_ft_in1k`](https://huggingface.co/zeromodels/convnext_large_fb_in22k_ft_in1k) |
| `convnext_large_fb_in22k_ft_in1k_384` | [`zeromodels/convnext_large_fb_in22k_ft_in1k_384`](https://huggingface.co/zeromodels/convnext_large_fb_in22k_ft_in1k_384) |
| `convnext_nano_d1h_in1k` | [`zeromodels/convnext_nano_d1h_in1k`](https://huggingface.co/zeromodels/convnext_nano_d1h_in1k) |
| `convnext_nano_in12k_ft_in1k` | [`zeromodels/convnext_nano_in12k_ft_in1k`](https://huggingface.co/zeromodels/convnext_nano_in12k_ft_in1k) |
| `convnext_pico_d1_in1k` | [`zeromodels/convnext_pico_d1_in1k`](https://huggingface.co/zeromodels/convnext_pico_d1_in1k) |
| `convnext_small_fb_in1k` | [`zeromodels/convnext_small_fb_in1k`](https://huggingface.co/zeromodels/convnext_small_fb_in1k) |
| `convnext_small_fb_in22k` | [`zeromodels/convnext_small_fb_in22k`](https://huggingface.co/zeromodels/convnext_small_fb_in22k) |
| `convnext_small_fb_in22k_ft_in1k` | [`zeromodels/convnext_small_fb_in22k_ft_in1k`](https://huggingface.co/zeromodels/convnext_small_fb_in22k_ft_in1k) |
| `convnext_small_fb_in22k_ft_in1k_384` | [`zeromodels/convnext_small_fb_in22k_ft_in1k_384`](https://huggingface.co/zeromodels/convnext_small_fb_in22k_ft_in1k_384) |
| `convnext_tiny_fb_in1k` | [`zeromodels/convnext_tiny_fb_in1k`](https://huggingface.co/zeromodels/convnext_tiny_fb_in1k) |
| `convnext_tiny_fb_in22k` | [`zeromodels/convnext_tiny_fb_in22k`](https://huggingface.co/zeromodels/convnext_tiny_fb_in22k) |
| `convnext_tiny_fb_in22k_ft_in1k` | [`zeromodels/convnext_tiny_fb_in22k_ft_in1k`](https://huggingface.co/zeromodels/convnext_tiny_fb_in22k_ft_in1k) |
| `convnext_tiny_fb_in22k_ft_in1k_384` | [`zeromodels/convnext_tiny_fb_in22k_ft_in1k_384`](https://huggingface.co/zeromodels/convnext_tiny_fb_in22k_ft_in1k_384) |
| `convnext_xlarge_fb_in22k` | [`zeromodels/convnext_xlarge_fb_in22k`](https://huggingface.co/zeromodels/convnext_xlarge_fb_in22k) |
| `convnext_xlarge_fb_in22k_ft_in1k` | [`zeromodels/convnext_xlarge_fb_in22k_ft_in1k`](https://huggingface.co/zeromodels/convnext_xlarge_fb_in22k_ft_in1k) |
| `convnext_xlarge_fb_in22k_ft_in1k_384` | [`zeromodels/convnext_xlarge_fb_in22k_ft_in1k_384`](https://huggingface.co/zeromodels/convnext_xlarge_fb_in22k_ft_in1k_384) |

## Tips

- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
- `ConvNeXtImageClassify` returns class logits; `ConvNeXtModel` 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: `ConvNeXtImageClassify.from_weights("hf:timm/convnext_xlarge.fb_in22k")`.

## Special Thanks

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

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