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Growth Rate:
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Sponsored by
Vmake Labs Video Enhancer
zeromodels / mobilevit_xs_cvnets_in1k
Model's Last Updated: August 30 2026
image-classification
Introduction of mobilevit_xs_cvnets_in1k
Model Details of mobilevit_xs_cvnets_in1k
See our collection for all versions of MobileViT.
Run MobileViT with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/mobilevit_xs_cvnets_in1k
Paper: MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer (arXiv:2110.02178) · HF Papers
MobileViT interleaves MobileNetV2 blocks with small transformers for mobile ImageNet classification (256). For Pascal VOC DeepLabV3 segmentation (512), use
update_mobilevit_deeplabv3_model_cards.py
.
For more details on the model, please go to the upstream model card .
Pure-
Keras 3
conversion of
timm/mobilevit_xs.cvnets_in1k
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is an
image-classification / backbone
checkpoint (
MobileViTImageClassify
/
MobileViTModel
).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.mobilevit import (
MobileViTImageClassify,
MobileViTModel,
MobileViTImageProcessor,
)
model = MobileViTImageClassify.from_weights("zeromodels/mobilevit_xs_cvnets_in1k")
processor = MobileViTImageProcessor.from_weights("zeromodels/mobilevit_xs_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 = MobileViTModel.from_weights(
"zeromodels/mobilevit_xs_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 variant the same way with
from_weights("zeromodels/<variant>")
:
| Variant | Hub |
|---|---|
mobilevit_s_cvnets_in1k
|
zeromodels/mobilevit_s_cvnets_in1k
|
mobilevit_xs_cvnets_in1k
|
zeromodels/mobilevit_xs_cvnets_in1k
|
mobilevit_xxs_cvnets_in1k
|
zeromodels/mobilevit_xxs_cvnets_in1k
|
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
MobileViTImageClassifyreturns class logits;MobileViTModelreturns features (as_backbone=Truefor multi-scale stages). - See docs and Loading Weights .
-
Upstream / timm checkpoints:
MobileViTImageClassify.from_weights("hf:timm/mobilevit_xs.cvnets_in1k").
Special Thanks
A huge thank you to the MobileViT authors and the timm / Hub communities for creating and releasing these models.
License: see YAML
license
(usually matches the upstream checkpoint).
Runs of zeromodels mobilevit_xs_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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30-day runs
More Information About mobilevit_xs_cvnets_in1k huggingface.co Model
Url of mobilevit_xs_cvnets_in1k
mobilevit_xs_cvnets_in1k huggingface.co Url
Provider of mobilevit_xs_cvnets_in1k huggingface.co
Other API from zeromodels
Updated:August 27 2026
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