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Sponsored by
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zeromodels / owlv2-large-patch14-ensemble
Model's Last Updated: August 27 2026
zero-shot-object-detection
Introduction of owlv2-large-patch14-ensemble
Model Details of owlv2-large-patch14-ensemble
See our collection for all versions of OWLv2.
Run OWLv2 with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/owlv2-large-patch14-ensemble
Paper: Scaling Open-Vocabulary Object Detection (arXiv:2306.09683) · HF Papers
OWLv2 keeps OWL-ViT's dual-tower skeleton and per-patch detection head, and scales it with self-training on web image-text pairs. It adds an objectness head (a learned is-this-patch-an-object score) and pads images to a square before resizing, which matters for post-processing target sizes.
For more details on the model, please go to Google's original model card .
Pure-
Keras 3
conversion of
google/owlv2-large-patch14-ensemble
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is an
open-vocabulary object detection
checkpoint (
Owlv2Detect
): pass free-text prompts at inference time.
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.owlv2 import (
Owlv2Detect,
Owlv2Processor,
Owlv2ImageProcessor,
)
model = Owlv2Detect.from_weights("zeromodels/owlv2-large-patch14-ensemble")
processor = Owlv2Processor.from_weights("zeromodels/owlv2-large-patch14-ensemble")
image_processor = Owlv2ImageProcessor.from_weights("zeromodels/owlv2-large-patch14-ensemble")
image = Image.open("your_image.jpg").convert("RGB")
prompts = ["a photo of a mug", "a photo of a knife"]
inputs = processor(text=[prompts], images=image)
output = model(
{
"input_ids": inputs["input_ids"],
"pixel_values": inputs["pixel_values"],
}
)
results = image_processor.post_process_object_detection(
output,
threshold=0.1,
target_sizes=[(image.height, image.width)],
text_labels=[prompts],
)[0]
for score, name, box in zip(
results["scores"], results["text_labels"], results["boxes"]
):
print(f"{name}: {float(score):.3f} {box}")
Load any OWL-ViT / OWLv2 variant the same way with
from_weights("zeromodels/<variant>")
(use
Owlv2Detect
for this repo):
| Variant | Hub | Family |
|---|---|---|
owlvit-base-patch32
|
zeromodels/owlvit-base-patch32
|
OWL-ViT |
owlvit-base-patch16
|
zeromodels/owlvit-base-patch16
|
OWL-ViT |
owlvit-large-patch14
|
zeromodels/owlvit-large-patch14
|
OWL-ViT |
owlv2-base-patch16
|
zeromodels/owlv2-base-patch16
|
OWLv2 |
owlv2-base-patch16-ensemble
|
zeromodels/owlv2-base-patch16-ensemble
|
OWLv2 |
owlv2-base-patch16-finetuned
|
zeromodels/owlv2-base-patch16-finetuned
|
OWLv2 |
owlv2-large-patch14
|
zeromodels/owlv2-large-patch14
|
OWLv2 |
owlv2-large-patch14-ensemble
|
zeromodels/owlv2-large-patch14-ensemble
|
OWLv2 |
owlv2-large-patch14-finetuned
|
zeromodels/owlv2-large-patch14-finetuned
|
OWLv2 |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
Prefer
Processor.from_weights(...)so image size matches the variant. -
Open-vocab thresholds are often much lower than closed-set detectors (try
0.1). -
OWLv2 pads to square before resize; pass the original
(height, width)astarget_sizescarefully (see the OWLv2 docs for the padding trap). - See OWLv2 docs and Loading Weights .
-
Community / upstream safetensors still work via the
hf:prefix, e.g.Owlv2Detect.from_weights("hf:google/owlv2-large-patch14-ensemble").
Special Thanks
A huge thank you to the Google OWLv2 authors for creating and releasing these models.
License: Apache 2.0.
Runs of zeromodels owlv2-large-patch14-ensemble on huggingface.co
73
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 owlv2-large-patch14-ensemble huggingface.co Model
Url of owlv2-large-patch14-ensemble
owlv2-large-patch14-ensemble huggingface.co Url
Provider of owlv2-large-patch14-ensemble huggingface.co
Other API from zeromodels
Updated:August 27 2026
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