README.md

---
tags:
- object-detection
- sam3
- segment-anything
- bounding-boxes
- uv-script
- generated
---

# Object Detection: Photograph Detection using sam3

This dataset contains object detection results (bounding boxes) for **photograph** detected in images from [NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset](https://huggingface.co/datasets/NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset) using Meta's SAM3 (Segment Anything Model 3).

**Generated using**: [uv-scripts/sam3](https://huggingface.co/datasets/uv-scripts/sam3) detection script

## Detection Statistics

- **Objects Detected**: photograph
- **Total Detections**: 4,500
- **Images with Detections**: 1,500 / 1,500 (100.0%)
- **Average Detections per Image**: 3.00

## Processing Details

- **Source Dataset**: [NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset](https://huggingface.co/datasets/NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset)
- **Model**: [facebook/sam3](https://huggingface.co/facebook/sam3)
- **Script Repository**: [uv-scripts/sam3](https://huggingface.co/datasets/uv-scripts/sam3)
- **Number of Samples Processed**: 1,500
- **Processing Time**: 2.2 minutes
- **Processing Date**: 2025-11-22 16:45 UTC

### Configuration

- **Image Column**: `image`
- **Dataset Split**: `train`
- **Class Name**: `photograph`
- **Confidence Threshold**: 0.5
- **Mask Threshold**: 0.5
- **Batch Size**: 8
- **Model Dtype**: bfloat16

## Model Information

SAM3 (Segment Anything Model 3) is Meta's state-of-the-art object detection and segmentation model that excels at:
- 🎯 **Zero-shot detection** - Detect objects using natural language prompts
- 📦 **Bounding boxes** - Accurate object localization
- 🎭 **Instance segmentation** - Pixel-perfect masks (not included in this dataset)
- 🖼️ **Any image domain** - Works on photos, documents, medical images, etc.

This dataset uses SAM3 in text-prompted detection mode to find instances of "photograph" in the source images.

## Dataset Structure

The dataset contains all original columns from the source dataset plus an `objects` column with detection results in HuggingFace object detection format (dict-of-lists):

- **bbox**: List of bounding boxes in `[x, y, width, height]` format (pixel coordinates)
- **category**: List of category indices (always `0` for single-class detection)
- **score**: List of confidence scores (0.0 to 1.0)

### Schema

```python
{
    "objects": {
        "bbox": [[x, y, w, h], ...],      # List of bounding boxes
        "category": [0, 0, ...],           # All same class
        "score": [0.95, 0.87, ...]        # Confidence scores
    }
}
```

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("{{output_dataset_id}}", split="train")

# Access detections for an image
example = dataset[0]
detections = example["objects"]

# Iterate through all detected objects in this image
for bbox, category, score in zip(
    detections["bbox"],
    detections["category"],
    detections["score"]
):
    x, y, w, h = bbox
    print(f"Detected photograph at ({x}, {y}) with confidence {score:.2f}")

# Filter high-confidence detections
high_conf_examples = [
    ex for ex in dataset
    if any(score > 0.8 for score in ex["objects"]["score"])
]

# Count total detections across dataset
total = sum(len(ex["objects"]["bbox"]) for ex in dataset)
print(f"Total detections: {total}")
```

## Visualization

To visualize the detections, you can use the visualization script from the same repository:

```bash
# Visualize first sample with detections
uv run https://huggingface.co/datasets/uv-scripts/sam3/raw/main/visualize-detections.py \
    {{output_dataset_id}} \
    --first-with-detections

# Visualize random samples
uv run https://huggingface.co/datasets/uv-scripts/sam3/raw/main/visualize-detections.py \
    {{output_dataset_id}} \
    --num-samples 5

# Save visualizations to files
uv run https://huggingface.co/datasets/uv-scripts/sam3/raw/main/visualize-detections.py \
    {{output_dataset_id}} \
    --num-samples 3 \
    --output-dir ./visualizations
```

## Reproduction

This dataset was generated using the [uv-scripts/sam3](https://huggingface.co/datasets/uv-scripts/sam3) object detection script:

```bash
uv run https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \
    <output-dataset> \
    --class-name photograph \
    --confidence-threshold 0.5 \
    --mask-threshold 0.5 \
    --batch-size 8 \
    --dtype bfloat16
```

### Running on HuggingFace Jobs (GPU)

This script requires a GPU. To run on HuggingFace infrastructure:

```bash
hf jobs uv run --flavor a100-large \
    -s HF_TOKEN=HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
    NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \
    <output-dataset> \
    --class-name photograph \
    --confidence-threshold 0.5
```

## Performance

- **Processing Speed**: ~11.4 images/second
- **GPU Configuration**: CUDA with bfloat16 precision

---

Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)