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tensorflow/lite/g3doc/models/image_classification/overview.md
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@ -22,8 +22,10 @@ We also provide <a href="example_applications">example applications</a> you can
use to get started.
If you are using a platform other than Android or iOS, or you are already
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
download our starter image classification model and the accompanying labels.
familiar with the
<a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite
APIs</a>, you can download our starter image classification model and the
accompanying labels.
<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip">Download
starter model and labels</a>
@ -33,13 +35,6 @@ experiment with different models to find the optimal balance between
performance, accuracy, and model size. For guidance, see
<a href="#choose_a_different_model">Choose a different model</a>.
If you are using a platform other than Android or iOS, or you are already
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
download our starter image classification model and the accompanying labels.
<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip">Download
starter model and labels</a>
### Example applications
We have example applications for image classification for both Android and iOS.
@ -216,9 +211,9 @@ performance, accuracy, and model size. For guidance, see
## Choose a different model
There are a large number of image classification models available on our
<a href="../../guide/hosted_models.md">List of hosted models</a>. You should aim to choose the
optimal model for your application based on performance, accuracy and model
size. There are trade-offs between each of them.
<a href="../../guide/hosted_models.md">List of hosted models</a>. You should aim
to choose the optimal model for your application based on performance, accuracy
and model size. There are trade-offs between each of them.
### Performance
@ -239,11 +234,11 @@ We measure accuracy in terms of how often the model correctly classifies an
image. For example, a model with a stated accuracy of 60% can be expected to
classify an image correctly an average of 60% of the time.
Our <a href="../../guide/hosted_models.md">list of hosted models</a> provides Top-1 and Top-5
accuracy statistics. Top-1 refers to how often the correct label appears as the
label with the highest probability in the model’s output. Top-5 refers to how
often the correct label appears in the top 5 highest probabilities in the
model’s output.
Our <a href="../../guide/hosted_models.md">list of hosted models</a> provides
Top-1 and Top-5 accuracy statistics. Top-1 refers to how often the correct label
appears as the label with the highest probability in the model’s output. Top-5
refers to how often the correct label appears in the top 5 highest probabilities
in the model’s output.
Our quantized Mobilenet models’ Top-5 accuracy ranges from 64.4 to 89.9%.
@ -258,8 +253,9 @@ Our quantized Mobilenet models’ size ranges from 0.5 to 3.4 Mb.
### Architecture
There are several different architectures of models available on
<a href="../../guide/hosted_models.md">List of hosted models</a>, indicated by the model’s name.
For example, you can choose between Mobilenet, Inception, and others.
<a href="../../guide/hosted_models.md">List of hosted models</a>, indicated by
the model’s name. For example, you can choose between Mobilenet, Inception, and
others.
The architecture of a model impacts its performance, accuracy, and size. All of
our hosted models are trained on the same data, meaning you can use the provided