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kerasformers/
flexivit_base_1000ep_in21k

Name: flexivit_base_1000ep_in21k
Availability: InStock

timm/flexivit_base.1000ep_in21k

finetune

apache-2.0

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

GitHub

zeromodels/flexivit_base_1000ep_in21k

Paper: FlexiViT: One Model for All Patch Sizes (arXiv:2212.08013) · HF Papers

FlexiViT trains one ViT that transfers across patch sizes at inference. Classifier or token backbone.

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

Pure-Keras 3 conversion of timm/flexivit_base.1000ep_in21k for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (FlexiViTImageClassify / FlexiViTModel).

✨ Quick start

python

import os

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

from PIL import Image
from zeromodels.models.flexivit import FlexiViTImageClassify, FlexiViTModel, FlexiViTImageProcessor

model = FlexiViTImageClassify.from_weights("zeromodels/flexivit_base_1000ep_in21k")
processor = FlexiViTImageProcessor.from_weights("zeromodels/flexivit_base_1000ep_in21k")

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 = FlexiViTModel.from_weights("zeromodels/flexivit_base_1000ep_in21k", as_backbone=True)
features = backbone(pixels, training=False)

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

Variant	Hub
`flexivit_base_1000ep_in21k`	`zeromodels/flexivit_base_1000ep_in21k`
`flexivit_base_1200ep_in1k`	`zeromodels/flexivit_base_1200ep_in1k`
`flexivit_base_300ep_in1k`	`zeromodels/flexivit_base_300ep_in1k`
`flexivit_base_300ep_in21k`	`zeromodels/flexivit_base_300ep_in21k`
`flexivit_large_1200ep_in1k`	`zeromodels/flexivit_large_1200ep_in1k`
`flexivit_large_300ep_in1k`	`zeromodels/flexivit_large_300ep_in1k`
`flexivit_large_600ep_in1k`	`zeromodels/flexivit_large_600ep_in1k`
`flexivit_small_1200ep_in1k`	`zeromodels/flexivit_small_1200ep_in1k`
`flexivit_small_300ep_in1k`	`zeromodels/flexivit_small_300ep_in1k`
`flexivit_small_600ep_in1k`	`zeromodels/flexivit_small_600ep_in1k`

Tips

Set KERAS_BACKEND before importing Keras / zeromodels.
FlexiViTImageClassify returns class logits; FlexiViTModel returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: FlexiViTImageClassify.from_weights("hf:timm/flexivit_base.1000ep_in21k").

Special Thanks

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

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

kerasformers/flexivit_base_1000ep_in21k