Sponsored by
PostMcpAi
zeromodels / dfine-nano
Model's Last Updated: August 27 2026
object-detection
Introduction of dfine-nano
Model Details of dfine-nano
See our collection for all versions of D-FINE.
Run D-FINE with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/dfine-nano
Paper: D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement (arXiv:2410.13842) · HF Papers
D-FINE is a real-time detector built on the RT-DETR recipe: an HGNetV2 backbone, a hybrid encoder, and a deformable decoder with 300 queries. It is NMS-free. Boxes are regressed via Fine-grained Distribution Refinement: each decoder layer predicts a distribution over discrete offset bins and accumulates refinements across layers.
For more details on the model, please go to the upstream model card .
Pure-
Keras 3
conversion of
ustc-community/dfine-nano-coco
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is an
object detection
checkpoint (
DFineDetect
) on COCO (HGNetV2-Nano).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.dfine import DFineDetect, DFineImageProcessor
model = DFineDetect.from_weights("zeromodels/dfine-nano")
processor = DFineImageProcessor.from_weights("zeromodels/dfine-nano")
image = Image.open("your_image.jpg").convert("RGB")
inputs = processor(image)
output = model(inputs["pixel_values"], training=False)
results = processor.post_process_object_detection(
output, threshold=0.5, target_sizes=[(image.height, image.width)]
)[0]
for score, name, box in zip(
results["scores"], results["label_names"], results["boxes"]
):
print(f"{name}: {float(score):.3f} {box}")
Load any D-FINE variant the same way with
from_weights("zeromodels/<variant>")
:
| Variant | Hub | Backbone |
|---|---|---|
dfine-nano
|
zeromodels/dfine-nano
|
HGNetV2-Nano |
dfine-small
|
zeromodels/dfine-small
|
HGNetV2-Small |
dfine-medium
|
zeromodels/dfine-medium
|
HGNetV2-Medium |
dfine-large
|
zeromodels/dfine-large
|
HGNetV2-Large |
dfine-xlarge
|
zeromodels/dfine-xlarge
|
HGNetV2-XLarge |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
DFineImageProcessorkeepsdo_normalize=Falseby default (rescaled[0, 1]input, matching upstream). - See D-FINE docs and Loading Weights .
-
Community / upstream safetensors still work via the
hf:prefix, e.g.DFineDetect.from_weights("hf:ustc-community/dfine-nano-coco").
Special Thanks
A huge thank you to the D-FINE authors (USTC community) for creating and releasing these models.
License: Apache 2.0.
Runs of zeromodels dfine-nano on huggingface.co
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More Information About dfine-nano huggingface.co Model
Url of dfine-nano
dfine-nano huggingface.co Url
Provider of dfine-nano huggingface.co
Other API from zeromodels
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