Total runs:
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Sponsored by
iMean AI Trip Planner
zeromodels / mobilevitv2_100_cvnets_in1k
Model's Last Updated: August 30 2026
image-classification
Introduction of mobilevitv2_100_cvnets_in1k
Model Details of mobilevitv2_100_cvnets_in1k
See our collection for all versions of MobileViT-V2.
Run MobileViT-V2 with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/mobilevitv2_100_cvnets_in1k
Paper: Separable Self-attention for Mobile Vision Transformers (arXiv:2206.02680) · HF Papers
MobileViTV2 replaces MHSA with separable self-attention (O(k)) and scales width via a single multiplier. Classification at 256/384; DeepLabV3 segmentation is a separate script/collection.
For more details on the model, please go to the upstream model card .
Pure-
Keras 3
conversion of
timm/mobilevitv2_100.cvnets_in1k
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is an
image-classification / backbone
checkpoint (
MobileViTV2ImageClassify
/
MobileViTV2Model
).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.mobilevitv2 import (
MobileViTV2ImageClassify,
MobileViTV2Model,
MobileViTV2ImageProcessor,
)
model = MobileViTV2ImageClassify.from_weights("zeromodels/mobilevitv2_100_cvnets_in1k")
processor = MobileViTV2ImageProcessor.from_weights("zeromodels/mobilevitv2_100_cvnets_in1k")
image = Image.open("your_image.jpg").convert("RGB")
logits = model(processor(image)["pixel_values"], training=False)
print(logits.shape) # (1, num_classes)
backbone = MobileViTV2Model.from_weights(
"zeromodels/mobilevitv2_100_cvnets_in1k", as_backbone=True
)
feats = backbone(processor(image)["pixel_values"], training=False)
print(len(feats), [tuple(f.shape) for f in feats])
Load any MobileViT-V2 variant the same way with
from_weights("zeromodels/<variant>")
:
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
MobileViTV2ImageClassifyreturns class logits;MobileViTV2Modelreturns features (as_backbone=Truefor multi-scale stages). - See docs and Loading Weights .
-
Upstream / timm checkpoints:
MobileViTV2ImageClassify.from_weights("hf:timm/mobilevitv2_100.cvnets_in1k").
Special Thanks
A huge thank you to the MobileViT-V2 authors and the timm / Hub communities for creating and releasing these models.
License: see YAML
license
(usually matches the upstream checkpoint).
Runs of zeromodels mobilevitv2_100_cvnets_in1k on huggingface.co
28
Total runs
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24-hour runs
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3-day runs
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7-day runs
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30-day runs
More Information About mobilevitv2_100_cvnets_in1k huggingface.co Model
Url of mobilevitv2_100_cvnets_in1k
mobilevitv2_100_cvnets_in1k huggingface.co Url
Provider of mobilevitv2_100_cvnets_in1k huggingface.co
Other API from zeromodels
Updated:August 27 2026
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