Vision Analysis
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D-FINE-Seg-L

dfine

transformer detector with HGNetv2 backbone

PaperCode
Parameters0.0M
GFLOPs0.0
Input Size640px
Best mAP-
LicenseApache-2.0

Architecture

Type

transformer

Backbone

HGNetv2

Neck

HybridEncoder

Head

FDR (Fine-grained Distribution Refinement)

Benchmark Results

Performance on COCO val2017 across different hardware configurations

No benchmarks yet

This model has not been benchmarked with LibreYOLO. Benchmarks coming soon.

Usage with LibreYOLO

from libreyolo import LibreYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LibreYOLO("LibreDFINEl.pt")

# Run inference
result = model("image.jpg", conf=0.25, iou=0.45)

# Process results
print(f"Found {len(result)} objects")
print(result.boxes.xyxy)   # bounding boxes (N, 4)
print(result.boxes.conf)   # confidence scores (N,)
print(result.boxes.cls)    # class IDs (N,)
detrnms-free

Related Models (dfine)

D-FINE-ND-FINE-SD-FINE-MD-FINE-LD-FINE-XD-FINE-Seg-ND-FINE-Seg-SD-FINE-Seg-MD-FINE-Seg-X

Run any model with one line

LibreYOLO has the best catalogue of state-of-the-art detectors, all behind one MIT-licensed Python API.

from libreyolo import LibreYOLO, SAMPLE_IMAGE

# LibreYOLO has the best catalogue of state-of-the-art models.
model = LibreYOLO("LibreRFDETRl.pt")           # RF-DETR-L (transformer flagship)
results = model(SAMPLE_IMAGE, save=True)        # run inference, save the annotated image

# Swap in any other model, same one-line API (weights auto-download):
#   LibreYOLO("LibreYOLO9c.pt")      # YOLO9-C
#   LibreYOLO("LibreYOLOXx.pt")      # YOLOX-X
#   LibreYOLO("LibreDFINEx.pt")      # D-FINE-X
#   LibreYOLO("LibreRTDETRr50.pt")   # RT-DETR-R50