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Sponsored by
PostMcpAi
zeromodels / tipsv2-b14-dpt
Model's Last Updated: August 27 2026
depth-estimation
Introduction of tipsv2-b14-dpt
Model Details of tipsv2-b14-dpt
See our collection for all versions of TIPSv2-DPT.
Run TIPSv2-DPT with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/tipsv2-b14-dpt
Paper: TIPSv2 (arXiv:2604.12012)
TIPSv2-DPT stacks DPT (Dense Prediction Transformer) heads on the TIPSv2 vision backbone. This single checkpoint serves three task classes: monocular depth estimation and semantic segmentation, or both at once.
For more details on the model, please go to the upstream model card .
Pure-
Keras 3
conversion of
google/tipsv2-b14-dpt
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
import numpy as np
import keras
from zeromodels.models.tipsv2_dpt import (
Tipsv2DptDensePredict, # depth + segmentation
Tipsv2DptDepthEstimation, # depth only
Tipsv2DptSemanticSegment, # segmentation only
Tipsv2DptImageProcessor,
)
# all three load from the SAME repo
model = Tipsv2DptDensePredict.from_weights("zeromodels/tipsv2-b14-dpt")
proc = Tipsv2DptImageProcessor(image_resolution=448)
image = Image.open("your_image.jpg").convert("RGB")
pixel_values = proc(np.array(image))["pixel_values"]
out = model(pixel_values)
depth = keras.ops.convert_to_numpy(out["predicted_depth"]) # (1, H', W')
seg = keras.ops.convert_to_numpy(out["segmentation_logits"]) # (1, H', W', num_labels)
# single-task variants (same weights, one output each)
depth_model = Tipsv2DptDepthEstimation.from_weights("zeromodels/tipsv2-b14-dpt")
seg_model = Tipsv2DptSemanticSegment.from_weights("zeromodels/tipsv2-b14-dpt")
Variants:
| Variant | Hub |
|---|---|
tipsv2-b14-dpt
|
zeromodels/tipsv2-b14-dpt
|
tipsv2-l14-dpt
|
zeromodels/tipsv2-l14-dpt
|
tipsv2-so400m14-dpt
|
zeromodels/tipsv2-so400m14-dpt
|
tipsv2-g14-dpt
|
zeromodels/tipsv2-g14-dpt
|
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
The image processor rescales to
[0, 1](no mean/std normalization); input resolution is 448. - Outputs are at the DPT feature resolution; resize to the input size for visualization.
-
Upstream checkpoint:
Tipsv2DptDensePredict.from_weights("hf:google/tipsv2-b14-dpt").
Special Thanks
A huge thank you to the TIPSv2 authors (Google DeepMind) and the HF community.
License: Apache-2.0 (matches the upstream
google/tipsv2-b14-dpt
checkpoint).
Runs of zeromodels tipsv2-b14-dpt on huggingface.co
37
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24-hour runs
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3-day runs
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30-day runs
More Information About tipsv2-b14-dpt huggingface.co Model
Url of tipsv2-b14-dpt
tipsv2-b14-dpt huggingface.co Url
Provider of tipsv2-b14-dpt huggingface.co
Other API from zeromodels
Updated:August 27 2026
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