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zeromodels / beit-large-patch16-512
Model's Last Updated: August 30 2026
image-classification
Introduction of beit-large-patch16-512
Model Details of beit-large-patch16-512
See our collection for all versions of BEiT.
Run BEiT with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/beit-large-patch16-512
Paper: BEiT: BERT Pre-Training of Image Transformers (arXiv:2106.08254) · HF Papers
BEiT is a ViT-family vision transformer with a per-layer relative position bias, a learnable layer scale on each residual branch, and mean pooling of the patch tokens. Large backbone fine-tuned on ImageNet-1k at 512x512 (1000 classes).
For more details on the model, please go to Microsoft's original model card .
Pure-
Keras 3
conversion of
microsoft/beit-large-patch16-512
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is a
image classification
checkpoint (
BeitImageClassify
).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.beit import BeitImageClassify, BeitModel, BeitImageProcessor
model = BeitImageClassify.from_weights("zeromodels/beit-large-patch16-512")
processor = BeitImageProcessor.from_weights("zeromodels/beit-large-patch16-512")
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 = BeitModel.from_weights("zeromodels/beit-large-patch16-512", as_backbone=True)
features = backbone(pixels, training=False)
Load any BEiT variant the same way with
from_weights("zeromodels/<variant>")
:
| Variant | Hub | Task |
|---|---|---|
beit-base-patch16-224
|
zeromodels/beit-base-patch16-224
|
image classification |
beit-large-patch16-224
|
zeromodels/beit-large-patch16-224
|
image classification |
beit-large-patch16-512
|
zeromodels/beit-large-patch16-512
|
image classification |
beit-base-patch16-224-pt22k-ft22k
|
zeromodels/beit-base-patch16-224-pt22k-ft22k
|
image classification |
beit-large-patch16-224-pt22k-ft22k
|
zeromodels/beit-large-patch16-224-pt22k-ft22k
|
image classification |
beit-base-finetuned-ade-640-640
|
zeromodels/beit-base-finetuned-ade-640-640
|
semantic segmentation |
beit-large-finetuned-ade-640-640
|
zeromodels/beit-large-finetuned-ade-640-640
|
semantic segmentation |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
Normalization (0.5/0.5) is baked into the model, so pass raw
[0, 255]pixels. -
Classification uses
BeitImageClassify; semantic segmentation usesBeitSemanticSegmentand returns logits at a quarter of the input resolution (upsample theargmaxmap to the input size). -
BeitModel.from_weights(..., as_backbone=True)returns the per-block token sequences for feature extraction. - See BEiT docs and Loading Weights .
-
Community / upstream safetensors still work via the
hf:prefix, e.g.BeitImageClassify.from_weights("hf:microsoft/beit-large-patch16-512").
Special Thanks
A huge thank you to the Microsoft Research BEiT authors for creating and releasing these models.
License: Apache 2.0.
Runs of zeromodels beit-large-patch16-512 on huggingface.co
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30-day runs
More Information About beit-large-patch16-512 huggingface.co Model
Url of beit-large-patch16-512
beit-large-patch16-512 huggingface.co Url
Provider of beit-large-patch16-512 huggingface.co
Other API from zeromodels
Updated:August 27 2026
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