Inference

objdet provides both a Python API and a CLI for running inference on trained models.

Python API

The Predictor class is the main entry point for inference.

Basic Usage

from objdet.inference import Predictor

# Load from checkpoint
predictor = Predictor.from_checkpoint(
    checkpoint_path="checkpoints/model.ckpt",
    device="cuda",
    confidence_threshold=0.3
)

# Predict single image
result = predictor.predict("images/test.jpg")
print(f"Found {len(result['boxes'])} objects")

# Predict batch
results = predictor.predict_batch(["img1.jpg", "img2.jpg"])

Sliced Inference (SAHI)

For large images (e.g., satellite/aerial), use SlicedInference to detect small objects by processing image tiles.

from objdet.inference import SlicedInference

# Initialize SAHI wrapper around predictor
sahi = SlicedInference(
    predictor=predictor,
    slice_height=640,
    slice_width=640,
    overlap_ratio=0.2,
    merge_method="nms"  # or "wbf"
)

# Run inference
result = sahi.predict("large_image.tiff")

Command Line Interface

You can also run inference directly from the terminal.

objdet predict

Run inference on an image or directory.

objdet predict \
    --config configs/model/faster_rcnn.yaml \
    --ckpt_path checkpoints/model.ckpt \
    --data.predict_data_path data/test_images \
    --trainer.accelerator gpu

objdet serve

Start a REST API for model serving (using LitServe).

objdet serve --config configs/serving/default.yaml

Output Format

Predictions are returned as a dictionary:

• boxes: (N, 4) tensor of bounding boxes [x1, y1, x2, y2].

• labels: (N,) tensor of class indices.

• scores: (N,) tensor of confidence scores.