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pipeline_tag: zero-shot-object-detection
license: apache-2.0
base_model: google/owlvit-base-patch16
library_name: kerasformers
tags:
- keras
- kerasformers
- owlvit
- open-vocabulary
- zero-shot-object-detection
- object-detection
- arxiv:2205.06230
- pytorch
- jax
- tf
---
## ***See [our collection](https://huggingface.co/collections/kerasformers/owl-vit-6a6a7af2bb61206cd50397ff) for all versions of OWL-ViT.***
# Run OWL-ViT with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/KerasFormers) [](https://imvision12.github.io/KerasFormers/owlvit/) [](https://huggingface.co/collections/kerasformers/owl-vit-6a6a7af2bb61206cd50397ff)
# kerasformers/owlvit-base-patch16
Paper: [Simple Open-Vocabulary Object Detection with Vision Transformers (arXiv:2205.06230)](https://arxiv.org/abs/2205.06230) · [HF Papers](https://huggingface.co/papers/2205.06230)
OWL-ViT detects objects described by free text, with no fixed class list. It starts from a CLIP-style vision and text encoder, then drops CLIP's pooling and attaches a lightweight box head to every patch token. Each patch becomes a detection candidate, scored by cosine similarity against your text queries.
For more details on the model, please go to Google's original [model card](https://huggingface.co/google/owlvit-base-patch16).
Pure-**Keras 3** conversion of [`google/owlvit-base-patch16`](https://huggingface.co/google/owlvit-base-patch16) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
This is an **open-vocabulary object detection** checkpoint (`OwlViTDetect`): pass free-text prompts at inference time.
## ✨ Quick start
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from kerasformers.models.owlvit import (
OwlViTDetect,
OwlViTProcessor,
OwlViTImageProcessor,
)
model = OwlViTDetect.from_weights("kerasformers/owlvit-base-patch16")
processor = OwlViTProcessor.from_weights("kerasformers/owlvit-base-patch16")
image_processor = OwlViTImageProcessor.from_weights("kerasformers/owlvit-base-patch16")
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("kerasformers/<variant>")` (use `OwlViTDetect` for this repo):
| Variant | Hub | Family |
|---|---|---|
| `owlvit-base-patch32` | [`kerasformers/owlvit-base-patch32`](https://huggingface.co/kerasformers/owlvit-base-patch32) | OWL-ViT |
| `owlvit-base-patch16` | [`kerasformers/owlvit-base-patch16`](https://huggingface.co/kerasformers/owlvit-base-patch16) | OWL-ViT |
| `owlvit-large-patch14` | [`kerasformers/owlvit-large-patch14`](https://huggingface.co/kerasformers/owlvit-large-patch14) | OWL-ViT |
| `owlv2-base-patch16` | [`kerasformers/owlv2-base-patch16`](https://huggingface.co/kerasformers/owlv2-base-patch16) | OWLv2 |
| `owlv2-base-patch16-ensemble` | [`kerasformers/owlv2-base-patch16-ensemble`](https://huggingface.co/kerasformers/owlv2-base-patch16-ensemble) | OWLv2 |
| `owlv2-base-patch16-finetuned` | [`kerasformers/owlv2-base-patch16-finetuned`](https://huggingface.co/kerasformers/owlv2-base-patch16-finetuned) | OWLv2 |
| `owlv2-large-patch14` | [`kerasformers/owlv2-large-patch14`](https://huggingface.co/kerasformers/owlv2-large-patch14) | OWLv2 |
| `owlv2-large-patch14-ensemble` | [`kerasformers/owlv2-large-patch14-ensemble`](https://huggingface.co/kerasformers/owlv2-large-patch14-ensemble) | OWLv2 |
| `owlv2-large-patch14-finetuned` | [`kerasformers/owlv2-large-patch14-finetuned`](https://huggingface.co/kerasformers/owlv2-large-patch14-finetuned) | OWLv2 |
## Tips
- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
- 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)` as `target_sizes` carefully (see the OWLv2 docs for the padding trap).
- See [OWL-ViT docs](https://imvision12.github.io/KerasFormers/owlvit/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
- Community / upstream safetensors still work via the `hf:` prefix, e.g. `OwlViTDetect.from_weights("hf:google/owlvit-base-patch16")`.
## Special Thanks
A huge thank you to the Google OWL-ViT authors for creating and releasing these models.
License: Apache 2.0.
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