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zeromodels / beit-base-finetuned-ade-640-640
Model's Last Updated: August 30 2026
image-segmentation
Introduction of beit-base-finetuned-ade-640-640
Model Details of beit-base-finetuned-ade-640-640
See our collection for all versions of BEiT.
Run BEiT with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/beit-base-finetuned-ade-640-640
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. Base backbone with a UPerNet head fine-tuned on ADE20K (150 classes).
For more details on the model, please go to Microsoft's original model card .
Pure-
Keras 3
conversion of
microsoft/beit-base-finetuned-ade-640-640
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is a
semantic segmentation
checkpoint (
BeitSemanticSegment
).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.beit import BeitSemanticSegment, BeitImageProcessor
model = BeitSemanticSegment.from_weights("zeromodels/beit-base-finetuned-ade-640-640")
processor = BeitImageProcessor.from_weights("zeromodels/beit-base-finetuned-ade-640-640") # resize 640 + post-processing
image = Image.open("scene.jpg").convert("RGB")
pixels = processor(image) # (1, 640, 640, 3), raw [0, 255]; the model normalizes
logits = model(pixels, training=False) # (1, H/4, W/4, 150), channels_last
seg = processor.post_process_semantic_segmentation(
logits, target_sizes=[image.size[::-1]] # (height, width)
)[0] # per-pixel ADE20K label map at full resolution
Load the segmentation checkpoints the same way with
from_weights("zeromodels/<variant>")
(the classification variants are in the
collection
):
| Variant | Hub | Task |
|---|---|---|
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;BeitImageProcessorhandles the resize and does not normalize. -
BeitSemanticSegmentreturns logits at a quarter of the input resolution.BeitImageProcessor.post_process_semantic_segmentation(logits, target_sizes=[(H, W)])upsamples them to your image size (bilinear, then argmax) and returns a per-pixel label map. -
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.BeitSemanticSegment.from_weights("hf:microsoft/beit-base-finetuned-ade-640-640").
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-base-finetuned-ade-640-640 on huggingface.co
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More Information About beit-base-finetuned-ade-640-640 huggingface.co Model
Url of beit-base-finetuned-ade-640-640
beit-base-finetuned-ade-640-640 huggingface.co Url
Provider of beit-base-finetuned-ade-640-640 huggingface.co
Other API from zeromodels
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