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README.md

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+---
+pipeline_tag: image-classification
+library_name: tensorflow
+tags:
+  - object-detection
+  - helicopter
+  - tflite
+  - transfer-learning
+  - efficientnet
+  - efficientnet-v2
+  - image-classification
+license: apache-2.0
+base_model: google/efficientnet-b4
+datasets:
+  - dcskycam/helicopter-classification
+---
+
+# DCSkyCam Helicopter Binary Classifier
+
+A TensorFlow Lite model that classifies cropped images of sky objects as **helicopter** or **not_helicopter**.
+
+This model is part of the [DCSkyCam](https://github.com/dcskycam) project — an AI-enabled sky monitoring system built on Raspberry Pi that automatically detected and identified helicopters in its field of view.
+
+## Model Overview
+
+| Property | Value |
+|----------|-------|
+| Architecture | EfficientNet-B4 (transfer learning via TensorFlow Hub `make_image_classifier` tool) |
+| Input size | 224 × 224 RGB |
+| Output | 2 classes: `helicopter`, `not_helicopter` |
+| Format | TensorFlow Lite (`.tflite`) |
+| Quantization | Post-training float16 quantization |
+| File size | ~70 MB |
+
+## Intended Use
+
+This model is designed to filter objects detected by the SSD MobileNet object detector in the DCSkyCam pipeline. When a candidate region is identified, this classifier determines whether it contains a helicopter before proceeding to type identification.
+
+**Intended for:** Sky monitoring, aviation observation, automated photography systems.
+
+**Not intended for:** Safety-critical applications, weapon systems, or any use that could cause harm.
+
+## Training Details
+
+- **Base model:** EfficientNet-B4 feature vector
+- **Training tool:** TensorFlow Hub `make_image_classifier` with transfer learning
+- **Positive samples:** 3,374 helicopter images
+- **Negative samples:** 2,755 non-helicopter images (birds, clouds, aircraft, ground objects)
+- **Training dataset:** [dcskycam/helicopter-classification](https://huggingface.co/datasets/dcskycam/helicopter-classification)
+  - Note: A small number of additional copyrighted images (<100) were used for training but are also excluded from the dataset published to HF for licensing reasons.
+
+## Performance
+
+| Class | Precision | Recall | F1 | 
+|-------|-----------|--------|-----|
+| helicopter | 0.9750 | 1.0000 | 0.9873 | 
+| not_helicopter | 1.0000 | 0.9667 | 0.9831 | 
+| **Macro Avg** | **0.9875** | **0.9833** | **0.9852** |
+
+Overall accuracy: **98.6%** 
+
+## Limitations & Biases
+
+- Trained on images captured from a fixed camera position with Raspberry Pi HQ Camera and wide-angle lens. Model output will likely drop on other image sources.
+- Performance may degrade with significantly different lighting conditions (dusk/dawn/night)
+- Small or distant helicopters that appear as tiny pixels may not be classified reliably
+- The model was trained on a relatively small dataset (~6,100 images)
+
+## Usage
+
+### Python (TensorFlow Lite Runtime on Raspberry Pi)
+
+```python
+import tflite_runtime.interpreter as tflite
+import numpy as np
+from PIL import Image
+
+# Load model and labels
+interpreter = tflite.Interpreter(model_path="custom_heli.tflite")
+interpreter.allocate_tensors()
+
+with open("custom_heli-labels.txt", "r") as f:
+    labels = [line.strip() for line in f.readlines()]
+
+# Prepare image (224x224, normalized to [0, 1])
+img = Image.open("candidate.jpg").convert("RGB")
+img = img.resize((224, 224))
+input_data = np.expand_dims(np.array(img, dtype=np.float32) / 255.0, axis=0)
+
+# Run inference
+input_details = interpreter.get_input_details()[0]
+interpreter.set_tensor(input_details["index"], input_data)
+interpreter.invoke()
+
+# Get results
+output = interpreter.get_tensor(interpreter.get_output_details()[0]["index"])[0]
+pred_idx = int(np.argmax(output))
+confidence = float(np.max(output))
+
+print(f"Prediction: {labels[pred_idx]} (confidence: {confidence:.4f})")
+```
+
+### Python (TensorFlow on Desktop)
+
+```python
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load model
+interpreter = tf.lite.Interpreter(model_path="custom_heli.tflite")
+interpreter.allocate_tensors()
+
+# Load labels
+with open("custom_heli-labels.txt", "r") as f:
+    labels = [line.strip() for line in f.readlines()]
+
+# Prepare and run inference (same as above)
+img = Image.open("candidate.jpg").convert("RGB")
+img = img.resize((224, 224))
+input_data = np.expand_dims(np.array(img, dtype=np.float32) / 255.0, axis=0)
+
+input_details = interpreter.get_input_details()[0]
+interpreter.set_tensor(input_details["index"], input_data)
+interpreter.invoke()
+
+output = interpreter.get_tensor(interpreter.get_output_details()[0]["index"])[0]
+pred_idx = int(np.argmax(output))
+confidence = float(np.max(output))
+
+print(f"Prediction: {labels[pred_idx]} (confidence: {confidence:.4f})")
+```
+
+## Citation
+
+If you use this model in your research, please cite the DCSkyCam project:
+
+```bibtex
+@misc{dcskycam2024,
+  title = {DCSkyCam: AI-Enabled Sky Monitoring System},
+  author = {DCSkyCam Contributors},
+  year = {2024},
+  url = {https://github.com/dcskycam}
+}
+```
+
+## License
+
+This project is licensed under the Apache License 2.0 — see [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details. The base model (EfficientNet-B4) is derived from TensorFlow Hub and subject to its own license terms.

custom_heli-labels.txt

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+helicopter
+not_helicopter

custom_heli.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c5c432ef38d549fddefbf209658de56dbf07562cb971eb64785d4ec3f79fa40
+size 70095856

inference.py

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+#!/usr/bin/env python3
+"""Inference script for the DCSkyCam helicopter binary classifier.
+
+Usage:
+    python inference.py <image_path>
+
+Requires: tflite-runtime (on Raspberry Pi) or tensorflow (on desktop).
+"""
+
+import sys
+import numpy as np
+from PIL import Image
+
+try:
+    import tflite_runtime.interpreter as tflite
+except ImportError:
+    import tensorflow.lite as tflite
+
+MODEL_PATH = "custom_heli.tflite"
+LABELS_PATH = "custom_heli-labels.txt"
+
+
+def load_labels(path):
+    with open(path, "r") as f:
+        return [line.strip() for line in f.readlines()]
+
+
+def predict(image_path, model_path=MODEL_PATH, labels_path=LABELS_PATH):
+    """Run inference on an image and return prediction."""
+    # Load model
+    interpreter = tflite.Interpreter(model_path=model_path)
+    interpreter.allocate_tensors()
+
+    # Load labels
+    labels = load_labels(labels_path)
+
+    # Prepare image: 224x224, RGB, normalized to [0, 1]
+    img = Image.open(image_path).convert("RGB")
+    img = img.resize((224, 224))
+    input_data = np.expand_dims(np.array(img, dtype=np.float32) / 255.0, axis=0)
+
+    # Run inference
+    input_details = interpreter.get_input_details()[0]
+    interpreter.set_tensor(input_details["index"], input_data)
+    interpreter.invoke()
+
+    # Get results
+    output = interpreter.get_tensor(interpreter.get_output_details()[0]["index"])[0]
+
+    pred_idx = int(np.argmax(output))
+    confidence = float(np.max(output))
+
+    return labels[pred_idx], confidence, output.tolist()
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print(f"Usage: {sys.argv[0]} <image_path>")
+        sys.exit(1)
+
+    image_path = sys.argv[1]
+    label, confidence, scores = predict(image_path)
+
+    print(f"Image: {image_path}")
+    print(f"Prediction: {label}")
+    print(f"Confidence: {confidence:.4f}")
+    for i, (lbl, sc) in enumerate(zip(load_labels(LABELS_PATH), scores)):
+        print(f"  {lbl}: {sc:.4f}")