Initial commit: Trashpanzee TFLite model weights, card, and inference script

README.md

@@ -0,0 +1,95 @@
+---
+language:
+- en
+license: mit
+tags:
+- image-classification
+- tflite
+- teachable-machine
+- mobilenet
+- waste-management
+pipeline_tag: image-classification
+---
+
+# Trashpanzee: Trash Can vs. Not Trash Classifier
+
+Trashpanzee is a lightweight, edge-ready computer vision model designed to detect waste receptacles and trash cans in images.
+
+## Model Summary
+- **Architecture**: MobileNetV2
+- **Format**: TensorFlow Lite (`model_unquant.tflite`, Float32)
+- **Model Size**: ~2.09 MB
+- **Input Resolution**: 224 x 224 RGB
+- **Input Normalization**: `(pixel_value / 127.5) - 1.0` (Values scaled to `[-1.0, 1.0]`)
+- **Classes**:
+  - `0`: `Trash Can`
+  - `1`: `Not Trash`
+
+## Files Included
+- `model_unquant.tflite`: Unquantized floating-point TFLite model weights.
+- `labels.txt`: Class index mapping.
+- `example_inference.py`: Python script for loading and testing inference.
+
+## Quickstart (Python)
+
+### 1. Install Dependencies
+```bash
+pip install huggingface_hub ai-edge-litert pillow numpy
+```
+
+### 2. Run Inference
+```python
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+# Multi-fallback interpreter import
+try:
+    from ai_edge_litert.interpreter import Interpreter
+except ImportError:
+    try:
+        from tflite_runtime.interpreter import Interpreter
+    except ImportError:
+        import tensorflow as tf
+        Interpreter = tf.lite.Interpreter
+
+# Download from Hugging Face Hub
+repo_id = "HalfDemon09I/trashpanzee-classifier"  # Replace with target repo if different
+model_path = hf_hub_download(repo_id=repo_id, filename="model_unquant.tflite")
+labels_path = hf_hub_download(repo_id=repo_id, filename="labels.txt")
+
+# Read labels (with typo cleanup: 'Not Tash' -> 'Not Trash')
+labels = []
+with open(labels_path, "r", encoding="utf-8") as f:
+    for line in f:
+        line = line.strip()
+        if line:
+            name = line.split(" ", 1)[-1]
+            labels.append("Not Trash" if name.lower() == "not tash" else name)
+
+# Initialize interpreter
+interpreter = Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Load and preprocess input image
+image = Image.open("sample.jpg").convert("RGB").resize((224, 224))
+input_data = (np.array(image, dtype=np.float32) / 127.5) - 1.0
+input_data = np.expand_dims(input_data, axis=0)
+
+# Run inference
+interpreter.set_tensor(input_details[0]["index"], input_data)
+interpreter.invoke()
+output_data = interpreter.get_tensor(output_details[0]["index"])[0]
+
+# Display predictions
+results = {labels[i]: float(output_data[i]) for i in range(len(labels))}
+for label, score in sorted(results.items(), key=lambda x: x[1], reverse=True):
+    print(f"{label}: {score * 100:.2f}%")
+```
+
+## Intended Use & Applications
+- Mobile and edge IoT waste collection and tracking devices.
+- Robotic sorting and autonomous cleaning robots.
+- Educational and smart city recycling awareness applications.

example_inference.py

@@ -0,0 +1,71 @@
+"""
+Trashpanzee - Local / Hub Inference Verification Script
+"""
+import os
+import sys
+import numpy as np
+from PIL import Image
+
+# Multi-fallback interpreter import
+try:
+    from ai_edge_litert.interpreter import Interpreter
+except ImportError:
+    try:
+        from tflite_runtime.interpreter import Interpreter
+    except ImportError:
+        try:
+            import tensorflow as tf
+            Interpreter = tf.lite.Interpreter
+        except ImportError:
+            sys.exit(
+                "Error: No TFLite runtime found. Please install ai-edge-litert or tflite-runtime or tensorflow:\n"
+                "  pip install ai-edge-litert pillow numpy"
+            )
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+DEFAULT_MODEL = os.path.join(SCRIPT_DIR, "model_unquant.tflite")
+DEFAULT_LABELS = os.path.join(SCRIPT_DIR, "labels.txt")
+
+def load_labels(labels_file=DEFAULT_LABELS):
+    labels = []
+    if not os.path.exists(labels_file):
+        return ["Trash Can", "Not Trash"]
+    with open(labels_file, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                name = line.split(" ", 1)[-1]
+                labels.append("Not Trash" if name.lower() == "not tash" else name)
+    return labels
+
+def classify_image(image_path, model_path=DEFAULT_MODEL, labels_path=DEFAULT_LABELS):
+    labels = load_labels(labels_path)
+    
+    interpreter = Interpreter(model_path=model_path)
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+    
+    # Load and resize
+    image = Image.open(image_path).convert("RGB").resize((224, 224))
+    input_data = (np.array(image, dtype=np.float32) / 127.5) - 1.0
+    input_data = np.expand_dims(input_data, axis=0)
+    
+    # Inference
+    interpreter.set_tensor(input_details[0]["index"], input_data)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]["index"])[0]
+    
+    results = {labels[i]: float(output_data[i]) for i in range(len(labels))}
+    return results
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python example_inference.py <path_to_image>")
+        sys.exit(1)
+    
+    img_path = sys.argv[1]
+    scores = classify_image(img_path)
+    print(f"\nClassification results for '{img_path}':")
+    for lbl, prob in sorted(scores.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {lbl:12s}: {prob * 100:.2f}%")

labels.txt

@@ -0,0 +1,2 @@
+0 Trash Can
+1 Not Tash

model_unquant.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:958bfe72816d72be1a4d81076d32681452ab8c32d900304b4240f5b57ce82e86
+size 2090332