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kerasformers/
res2next50_in1k

Name: res2next50_in1k
Availability: InStock

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Run Res2Net with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

zeromodels/res2next50_in1k

Paper: Res2Net: A New Multi-scale Backbone Architecture (arXiv:1904.01169) · HF Papers

Res2Net represents multi-scale features at a granular level inside residual blocks. Available as classifier and feature backbone.

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

Pure-Keras 3 conversion of timm/res2next50.in1k for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (Res2NetImageClassify / Res2NetModel).

✨ Quick start

python

import os

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

from PIL import Image
from zeromodels.models.res2net import Res2NetImageClassify, Res2NetModel, Res2NetImageProcessor

model = Res2NetImageClassify.from_weights("zeromodels/res2next50_in1k")
processor = Res2NetImageProcessor.from_weights("zeromodels/res2next50_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 = Res2NetModel.from_weights("zeromodels/res2next50_in1k", as_backbone=True)
features = backbone(pixels, training=False)

Load any Res2Net variant the same way with from_weights("zeromodels/<variant>"):

Variant	Hub
`res2net101_26w_4s_in1k`	`zeromodels/res2net101_26w_4s_in1k`
`res2net50_14w_8s_in1k`	`zeromodels/res2net50_14w_8s_in1k`
`res2net50_26w_4s_in1k`	`zeromodels/res2net50_26w_4s_in1k`
`res2net50_26w_6s_in1k`	`zeromodels/res2net50_26w_6s_in1k`
`res2net50_26w_8s_in1k`	`zeromodels/res2net50_26w_8s_in1k`
`res2net50_48w_2s_in1k`	`zeromodels/res2net50_48w_2s_in1k`
`res2next50_in1k`	`zeromodels/res2next50_in1k`

Tips

Set KERAS_BACKEND before importing Keras / zeromodels.
Res2NetImageClassify returns class logits; Res2NetModel returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: Res2NetImageClassify.from_weights("hf:timm/res2next50.in1k").

Special Thanks

A huge thank you to the Res2Net authors and the timm / Hub communities for creating and releasing these models.

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

kerasformers/res2next50_in1k