kerasformers/mobilenetv2_140_ra_in1k

See our collection for all versions of MobileNetV2.

Run MobileNetV2 with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/mobilenetv2_140_ra_in1k

Paper: MobileNetV2: Inverted Residuals and Linear Bottlenecks (arXiv:1801.04381) · HF Papers

MobileNetV2 uses inverted residuals and linear bottlenecks for mobile classification and as a lightweight multi-scale backbone.

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

Pure-Keras 3 conversion of timm/mobilenetv2_140.ra_in1k for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (MobileNetV2ImageClassify / MobileNetV2Model).

✨ Quick start

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

from PIL import Image
import numpy as np
from kerasformers.models.mobilenetv2 import MobileNetV2ImageClassify, MobileNetV2Model

model = MobileNetV2ImageClassify.from_weights("kerasformers/mobilenetv2_140_ra_in1k")
backbone = MobileNetV2Model.from_weights(
    "kerasformers/mobilenetv2_140_ra_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 MobileNetV2 variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
mobilenetv2_050_lamb_in1k	kerasformers/mobilenetv2_050_lamb_in1k
mobilenetv2_100_ra_in1k	kerasformers/mobilenetv2_100_ra_in1k
mobilenetv2_110d_ra_in1k	kerasformers/mobilenetv2_110d_ra_in1k
mobilenetv2_120d_ra_in1k	kerasformers/mobilenetv2_120d_ra_in1k
mobilenetv2_140_ra_in1k	kerasformers/mobilenetv2_140_ra_in1k

Tips

• Set KERAS_BACKEND before importing Keras / kerasformers.
• MobileNetV2ImageClassify returns class logits; MobileNetV2Model returns features (as_backbone=True for multi-scale stages).
• See docs and Loading Weights.
• Upstream / timm checkpoints: MobileNetV2ImageClassify.from_weights("hf:timm/mobilenetv2_140.ra_in1k").

Special Thanks

A huge thank you to the MobileNetV2 authors and the timm / Hub communities for creating and releasing these models.

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