JZVince/predator_v2_fp16

SpotPredator — YOLO11n Farm Predator Detector (TFLite)

predator_v2_fp16.tflite is a fine-tuned YOLO11 nano object-detection model that runs on-device on a Raspberry Pi Zero 2 W to detect common farm predators in real time. It is the vision model behind SpotPredator, a solar-powered, LoRa-linked field device that watches over free-range poultry and alerts a display station when a predator appears.

🔗 Full project (hardware, enclosure, code, deployment): github.com/JZVince/spotpredator

• Task: object detection
• Base model: Ultralytics YOLO11n (nano), fine-tuned
• Format: TensorFlow Lite, FP16 quantized (fp16)
• Input: 640 × 640 RGB
• Runtime: tflite_runtime / ai-edge-litert on Raspberry Pi (CPU, XNNPACK)
• Detection classes used in deployment: coyote, fox, raptor
• Full label set: background, poultry, predator, coyote, fox, raptor

Intended use

Detecting farm predators (coyote, fox, raptor) from a fixed/rotating outdoor camera so a low-power edge device can trigger local alerts. Designed for low-resolution, small-object, edge-CPU conditions — predators often occupy only 20–40 px in a 1920×1080 frame, so the capture pipeline crops the sky band and tiles it into 640×640 patches before inference.

Out of scope / limitations

• Trained for a specific set of North-American farm predators; not a general wildlife detector.
• Small, distant, or heavily occluded animals may be missed.
• Performance varies with lighting, weather, and camera exposure.
• Not intended for safety-critical or human-detection use.

Training data

Fine-tuned on a mix of:

• Author-collected field images captured by the SpotPredator camera (Raspberry Pi Camera Module 3 / Arducam), representative of the real deployment (low-res, small objects, sky-cropped).
• LILA BC — Labeled Information Library of Alexandria: Biology and Conservation (camera-trap / wildlife imagery).
• GBIF — Global Biodiversity Information Facility occurrence media.

Please review and comply with the individual licenses/terms of the LILA and GBIF media used. Author-collected images are owned by the author.

Usage

# On a Raspberry Pi (or any TFLite host)
try:
    from tflite_runtime.interpreter import Interpreter
except ImportError:
    from ai_edge_litert.interpreter import Interpreter
import numpy as np

interpreter = Interpreter(model_path="predator_v2_fp16.tflite")
interpreter.allocate_tensors()
inp = interpreter.get_input_details()
out = interpreter.get_output_details()

# image: 640x640x3 RGB, normalized as your pipeline expects
interpreter.set_tensor(inp[0]['index'], image[None].astype(np.float32))
interpreter.invoke()
detections = interpreter.get_tensor(out[0]['index'])  # decode YOLO output (boxes + scores)

Confidence threshold used in the SpotPredator deployment: 0.7.

License

AGPL-3.0. This model is fine-tuned from Ultralytics YOLO11, which is licensed under AGPL-3.0; derivative models inherit AGPL-3.0. If you use this model as part of a networked service, the AGPL requires you to make your corresponding source available. For commercial use without AGPL obligations, obtain an Ultralytics Enterprise License.

Attribution

• Base model: Ultralytics YOLO11 (AGPL-3.0)
• Datasets: LILA BC, GBIF, and author-collected imagery
• Project: SpotPredator — farm predator detection on the edge

Citation

@software{spotpredator_yolo11n,
  title  = {SpotPredator: YOLO11n Farm Predator Detector (TFLite)},
  author = {JZVince},
  year   = {2026},
  url    = {https://huggingface.co/JZVince/predator_v2_fp16}
}