Add README.md for SAM3 object detection with usage instructions and examples

README.md

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+---
+viewer: false
+tags: [uv-script, computer-vision, object-detection, sam3, image-processing]
+license: apache-2.0
+---
+
+# 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:
+
+```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 \
+    input-dataset \
+    output-dataset \
+    --class-name photograph
+```
+
+## Local Execution
+
+If you have a CUDA GPU locally:
+
+```bash
+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
+
+<details>
+<summary>All options</summary>
+
+```
+--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)
+```
+
+</details>
+
+## HuggingFace Jobs Examples
+
+### Historical Newspapers
+
+Detect photographs in historical newspaper scans:
+
+```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 \
+    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:
+
+```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 \
+    my-documents \
+    documents-with-tables \
+    --class-name table
+```
+
+### Wildlife Camera Traps
+
+Detect animals in camera trap images:
+
+```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 \
+    wildlife-images \
+    wildlife-detections \
+    --class-name animal \
+    --confidence-threshold 0.5
+```
+
+### Quick Testing
+
+Test on a small subset before full run:
+
+```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 \
+    large-dataset \
+    test-output \
+    --class-name object \
+    --max-samples 20
+```
+
+### Using Different GPU Flavors
+
+```bash
+# L4 (cost-effective)
+--flavor l4x1
+
+# A100 (fastest)
+--flavor a100
+```
+
+See [HF Jobs pricing](https://huggingface.co/pricing#spaces-compute).
+
+## Output Format
+
+Adds `objects` column with ClassLabel-based detections:
+
+```python
+{
+    "objects": [
+        {
+            "bbox": [x, y, width, height],
+            "category": 0,  # Always 0 for single class
+            "score": 0.87
+        }
+    ]
+}
+```
+
+Load and use:
+
+```python
+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:
+
+```bash
+# 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](https://huggingface.co/facebook/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](https://huggingface.co/uv-scripts):
+
+- **dataset-creation** - Create HF datasets from files
+- **vllm** - Fast LLM inference
+- **ocr** - Document OCR
+
+## License
+
+Apache 2.0