SAM3 Object Detection

Detect objects in images using Meta's SAM3 (Segment Anything Model 3) with text prompts. Process HuggingFace datasets with zero-shot object detection using natural language descriptions.

Quick Start

Requires GPU. Use HuggingFace Jobs for cloud execution:

hf jobs uv run --flavor a100-large \
    -s HF_TOKEN=HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    input-dataset \
    output-dataset \
    --class-name photograph

Example Output

Here's an example of detected objects (photographs in historical newspapers) with bounding boxes and confidence scores:

Photograph detected in a historical newspaper with bounding box and confidence score. Generated from davanstrien/newspapers-image-predictions.

Local Execution

If you have a CUDA GPU locally:

uv run detect-objects.py INPUT OUTPUT --class-name CLASSNAME

Arguments

Required:

• input_dataset - Input HF dataset ID
• output_dataset - Output HF dataset ID
• --class-name - Object class to detect (e.g., "photograph", "animal", "table")

Common options:

• --confidence-threshold FLOAT - Min confidence (default: 0.5)
• --batch-size INT - Batch size (default: 4)
• --max-samples INT - Limit samples for testing
• --image-column STR - Image column name (default: "image")
• --private - Make output private

All options

--mask-threshold FLOAT       Mask generation threshold (default: 0.5)
--split STR                  Dataset split (default: "train")
--shuffle                    Shuffle before processing
--model STR                  Model ID (default: "facebook/sam3")
--dtype STR                  Precision: float32|float16|bfloat16
--hf-token STR               HF token (or use HF_TOKEN env var)

HuggingFace Jobs Examples

Historical Newspapers

Detect photographs in historical newspaper scans:

hf jobs uv run --flavor a100-large \
    -s HF_TOKEN=HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    davanstrien/newspapers-with-images-after-photography \
    my-username/newspapers-detected \
    --class-name photograph \
    --confidence-threshold 0.6 \
    --batch-size 8

Document Tables

Extract tables from document scans:

hf jobs uv run --flavor a100-large \
    -s HF_TOKEN=HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    my-documents \
    documents-with-tables \
    --class-name table

Wildlife Camera Traps

Detect animals in camera trap images:

hf jobs uv run --flavor a100-large \
    -s HF_TOKEN=HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    wildlife-images \
    wildlife-detections \
    --class-name animal \
    --confidence-threshold 0.5

Quick Testing

Test on a small subset before full run:

hf jobs uv run --flavor a100-large \
    -s HF_TOKEN=HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    large-dataset \
    test-output \
    --class-name object \
    --max-samples 20

Using Different GPU Flavors

# L4 (cost-effective)
--flavor l4x1

# A100 (fastest)
--flavor a100

See HF Jobs pricing.

Output Format

Adds objects column with ClassLabel-based detections:

{
    "objects": [
        {
            "bbox": [x, y, width, height],
            "category": 0,  # Always 0 for single class
            "score": 0.87
        }
    ]
}

Load and use:

from datasets import load_dataset

ds = load_dataset("username/output", split="train")

# ClassLabel feature preserves your class name
class_name = ds.features["objects"].feature["category"].names[0]
print(f"Detected class: {class_name}")

for sample in ds:
    for obj in sample["objects"]:
        print(f"{class_name}: {obj['score']:.2f} at {obj['bbox']}")

Detecting Multiple Object Types

To detect multiple object types, run the script multiple times with different --class-name values:

# Detect photographs
hf jobs uv run ... --class-name photograph

# Detect illustrations
hf jobs uv run ... --class-name illustration

# Merge results as needed

Performance

GPU	Batch Size	~Images/sec
L4	4-8	2-4
A10	8-16	4-6

Varies by image size and detection complexity

Common Use Cases

• Documents: --class-name table or --class-name figure
• Newspapers: --class-name photograph or --class-name illustration
• Wildlife: --class-name animal or --class-name bird
• Products: --class-name product or --class-name label

Troubleshooting

• No CUDA: Use HF Jobs (see examples above)
• OOM errors: Reduce --batch-size
• Few detections: Lower --confidence-threshold or try different class descriptions
• Wrong column: Use --image-column your_column_name

About SAM3

SAM3 is Meta's zero-shot vision model. Describe any object in natural language and it will detect it—no training required.

Note: This script uses transformers from git (SAM3 not yet in stable release).

See Also

More UV scripts at huggingface.co/uv-scripts:

• dataset-creation - Create HF datasets from files
• vllm - Fast LLM inference
• ocr - Document OCR

License

Apache 2.0