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kerasformers/
convnextv2_nano_fcmae_ft_in22k_in1k

Name: convnextv2_nano_fcmae_ft_in22k_in1k
Availability: InStock

timm/convnextv2_nano.fcmae_ft_in22k_in1k

finetune

cc-by-nc-4.0

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See our collection for all versions of ConvNeXt-V2.

Run ConvNeXt-V2 with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

zeromodels/convnextv2_nano_fcmae_ft_in22k_in1k

Paper: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders (arXiv:2301.00808) · HF Papers

ConvNeXt V2 adds Global Response Normalization and FCMAE pretraining. Same classifier / backbone split as ConvNeXt.

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

Pure-Keras 3 conversion of timm/convnextv2_nano.fcmae_ft_in22k_in1k for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (ConvNeXtV2ImageClassify / ConvNeXtV2Model).

✨ Quick start

python

import os

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

from PIL import Image
from zeromodels.models.convnextv2 import ConvNeXtV2ImageClassify, ConvNeXtV2Model, ConvNeXtV2ImageProcessor

model = ConvNeXtV2ImageClassify.from_weights("zeromodels/convnextv2_nano_fcmae_ft_in22k_in1k")
processor = ConvNeXtV2ImageProcessor.from_weights("zeromodels/convnextv2_nano_fcmae_ft_in22k_in1k")

image = Image.open("your_image.jpg").convert("RGB")
pixels = processor(image)  # resize + normalize (normalization lives in the processor)
logits = model(pixels, training=False)
print(logits.shape)  # (1, num_classes)

# Feature extraction: the backbone without the classifier head
backbone = ConvNeXtV2Model.from_weights("zeromodels/convnextv2_nano_fcmae_ft_in22k_in1k", as_backbone=True)
features = backbone(pixels, training=False)

Load any ConvNeXt-V2 variant the same way with from_weights("zeromodels/<variant>"):

Variant	Hub
`convnextv2_atto_fcmae_ft_in1k`	`zeromodels/convnextv2_atto_fcmae_ft_in1k`
`convnextv2_base_fcmae_ft_in1k`	`zeromodels/convnextv2_base_fcmae_ft_in1k`
`convnextv2_base_fcmae_ft_in22k_in1k`	`zeromodels/convnextv2_base_fcmae_ft_in22k_in1k`
`convnextv2_base_fcmae_ft_in22k_in1k_384`	`zeromodels/convnextv2_base_fcmae_ft_in22k_in1k_384`
`convnextv2_femto_fcmae_ft_in1k`	`zeromodels/convnextv2_femto_fcmae_ft_in1k`
`convnextv2_huge_fcmae_ft_in1k`	`zeromodels/convnextv2_huge_fcmae_ft_in1k`
`convnextv2_huge_fcmae_ft_in22k_in1k_384`	`zeromodels/convnextv2_huge_fcmae_ft_in22k_in1k_384`
`convnextv2_huge_fcmae_ft_in22k_in1k_512`	`zeromodels/convnextv2_huge_fcmae_ft_in22k_in1k_512`
`convnextv2_large_fcmae_ft_in1k`	`zeromodels/convnextv2_large_fcmae_ft_in1k`
`convnextv2_large_fcmae_ft_in22k_in1k`	`zeromodels/convnextv2_large_fcmae_ft_in22k_in1k`
`convnextv2_large_fcmae_ft_in22k_in1k_384`	`zeromodels/convnextv2_large_fcmae_ft_in22k_in1k_384`
`convnextv2_nano_fcmae_ft_in1k`	`zeromodels/convnextv2_nano_fcmae_ft_in1k`
`convnextv2_nano_fcmae_ft_in22k_in1k`	`zeromodels/convnextv2_nano_fcmae_ft_in22k_in1k`
`convnextv2_nano_fcmae_ft_in22k_in1k_384`	`zeromodels/convnextv2_nano_fcmae_ft_in22k_in1k_384`
`convnextv2_pico_fcmae_ft_in1k`	`zeromodels/convnextv2_pico_fcmae_ft_in1k`
`convnextv2_tiny_fcmae_ft_in1k`	`zeromodels/convnextv2_tiny_fcmae_ft_in1k`
`convnextv2_tiny_fcmae_ft_in22k_in1k`	`zeromodels/convnextv2_tiny_fcmae_ft_in22k_in1k`
`convnextv2_tiny_fcmae_ft_in22k_in1k_384`	`zeromodels/convnextv2_tiny_fcmae_ft_in22k_in1k_384`

Tips

Set KERAS_BACKEND before importing Keras / zeromodels.
ConvNeXtV2ImageClassify returns class logits; ConvNeXtV2Model returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: ConvNeXtV2ImageClassify.from_weights("hf:timm/convnextv2_nano.fcmae_ft_in22k_in1k").

Special Thanks

A huge thank you to the ConvNeXt-V2 authors and the timm / Hub communities for creating and releasing these models.

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

kerasformers/convnextv2_nano_fcmae_ft_in22k_in1k