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README.md

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+---
+license: apache-2.0
+tags: 
+- vision
+- image-classification
+---
+
+# Kindwise Router Classifier
+
+This model classifies images based on their content, acting as a "router" to
+direct requests to the correct Kindwise service.  It automatically detects
+whether an image contains human, insect, mushroom, or plant.
+
+Th model is intended to be the first step in an image processing pipeline.
+Instead of having each specialized service (e.g., insect, plant classification)
+analyze every image, this model quickly determines the image's category. This
+reduces latency and optimizes system resources.
+
+## Technical Details and Formats
+
+The model is available in two optimized formats for easy deployment:
+- **TorchScript**: Optimized for production environments and server-side applications where performance and low latency are critical.
+- **TensorFlow Lite**: Perfect for mobile devices and edge computing, where efficiency and minimal model size are key.
+
+## Usage
+
+Here is how to use this model to classify an image into one of the basic classes:
+
+### PyTorch
+
+```python
+from huggingface_hub import hf_hub_download
+import cv2
+import numpy as np
+import PIL.Image
+import torch
+import torchvision
+
+DEVICE_NAME = 'cuda:0'
+MODEL_PATH = hf_hub_download('kindwise/router.base', 'model.traced.pt')
+CLASSES_PATH = hf_hub_download('kindwise/router.base', 'classes.txt')
+IMAGE_PATH = '/tmp/photo.jpg' 
+
+with open(CLASSES_PATH) as f:
+    CLASSES = [line.strip() for line in f]
+MODEL = torch.jit.load(MODEL_PATH).eval().to(DEVICE_NAME)
+
+
+def resize_crop(image_data: np.ndarray, target_size: int = 480) -> np.ndarray | None:
+    height, width, _ = image_data.shape
+    # Determine the size of the square crop
+    crop_size = min(height, width)
+    # Calculate coordinates for center crop
+    start_x = (width - crop_size) // 2
+    start_y = (height - crop_size) // 2
+    # Perform center crop
+    cropped_img = image_data[
+        start_y : start_y + crop_size,
+        start_x : start_x + crop_size
+    ]
+    # Resize cropped image to target size
+    return  cv2.resize(
+        cropped_img,
+        (target_size, target_size),
+        interpolation=cv2.INTER_AREA,
+    )
+
+with torch.no_grad():
+    image_array = np.array(PIL.Image.open(IMAGE_PATH))
+    image_array_resized = resize_crop(image_array)
+    image_tensor = torchvision.transforms.functional.to_tensor(image_array_resized).to(DEVICE_NAME)
+    prediction = MODEL(image_tensor.unsqueeze(0)).squeeze(0).cpu().numpy()
+    for i in (-prediction).argsort():
+        print(f'{CLASSES[i]:>10}: {100 * prediction[i]:.1f}%')
+```
+
+Output:
+```
+          plant: 91.3%
+unhealthy_plant: 53.3%
+           crop: 16.2%
+         insect: 0.4%
+          human: 0.1%
+       mushroom: 0.0%
+```
+
+###
+
+### TensorFlow Lite
+
+```python
+from huggingface_hub import hf_hub_download
+import numpy as np
+import tensorflow as tf
+
+MODEL_PATH = hf_hub_download('kindwise/router.base', 'model.tflite')  # or model.optimized.tflite
+CLASSES_PATH = hf_hub_download('kindwise/router.base', 'classes.txt')
+
+with open(CLASSES_PATH) as f:
+    CLASSES = [line.strip() for line in f]
+INTERPRETER = tf.lite.Interpreter(model_path=MODEL_PATH)
+INTERPRETER.allocate_tensors()
+
+image_array_resized = ... # see the previous example
+tf_input = np.expand_dims(  # add batch dimension
+    (image_array_resized / 255).astype(np.float32),  # image values in [0..1]
+    0,
+)
+input_details = INTERPRETER.get_input_details()
+output_details = INTERPRETER.get_output_details()
+INTERPRETER.set_tensor(
+    input_details[0]['index'],
+    tf_input,
+)
+INTERPRETER.invoke()
+logits = INTERPRETER.get_tensor(output_details[0]['index'])[0]
+prediction = tf.nn.sigmoid(logits).numpy()
+for i in (-prediction).argsort():
+    print(f'{CLASSES[i]:>10}: {100 * prediction[i]:.1f}%')
+```
+
+Output:
+```
+          plant: 91.3%
+unhealthy_plant: 53.3%
+           crop: 16.2%
+         insect: 0.4%
+          human: 0.1%
+       mushroom: 0.0%
+```

classes.txt

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+crop
+human
+insect
+mushroom
+plant
+unhealthy_plant

config.json

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+{
+  "architecture": "caformer_b36",
+  "id2label": {
+    "0": "crop",
+    "1": "human",
+    "2": "insect",
+    "3": "mushroom",
+    "4": "plant"
+    "6": "unhealthy_plant",
+  },
+  "label2id": {
+    "crop": 0,
+    "human": 1,
+    "insect": 2,
+    "mushroom": 3,
+    "plant": 4,
+    "unhealthy_plant": 5
+  },
+  "image_size": 480
+}

model.optimized.tflite

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+size 96167688

model.tflite

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model.traced.pt

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