chore: import upstream snapshot with attribution

2026-07-13 12:14:16 +08:00
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+# TensorFlow Lite Python image classification demo
+
+This `label_image.py` script shows how you can load a pre-trained and converted
+TensorFlow Lite model and use it to recognize objects in images. The Python
+script accepts arguments specifying the model to use, the corresponding labels
+file, and the image to process.
+
+**Tip:** If you're using a Raspberry Pi, instead try the
+[classify_picamera.py example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/raspberry_pi).
+
+Before you begin, make sure you
+[have TensorFlow installed](https://www.tensorflow.org/install).
+
+## Download sample model and image
+
+You can use any compatible model, but the following MobileNet v1 model offers a
+good demonstration of a model trained to recognize 1,000 different objects.
+
+```sh
+# Get photo
+curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp > /tmp/grace_hopper.bmp
+# Get model
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp
+# Get labels
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz  | tar xzv -C /tmp  mobilenet_v1_1.0_224/labels.txt
+
+mv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/
+```
+
+## Run the sample
+
+```sh
+python3 label_image.py \
+  --model_file /tmp/mobilenet_v1_1.0_224.tflite \
+  --label_file /tmp/labels.txt \
+  --image /tmp/grace_hopper.bmp
+```
+
+You should see results like this:
+
+```
+0.728693: military uniform
+0.116163: Windsor tie
+0.035517: bow tie
+0.014874: mortarboard
+0.011758: bolo tie
+```