From c8a93d69b282467be127fdbde1f78c1bdc8b1671 Mon Sep 17 00:00:00 2001 From: Anirudh Sriram Date: Mon, 19 Oct 2020 09:01:31 -0700 Subject: [PATCH] Fixing a typo PiperOrigin-RevId: 337865514 Change-Id: I28f978ea67b42607c44cafe2736d2f015315d982 --- tensorflow/lite/g3doc/models/image_classification/overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/lite/g3doc/models/image_classification/overview.md b/tensorflow/lite/g3doc/models/image_classification/overview.md index b8075bb872a..a9b1d49e0dc 100644 --- a/tensorflow/lite/g3doc/models/image_classification/overview.md +++ b/tensorflow/lite/g3doc/models/image_classification/overview.md @@ -24,7 +24,7 @@ started. You can leverage the out-of-box API from TensorFlow Lite Task Library to [integrate image classification models](../../inference_with_metadata/task_library/image_classifier) in just a few lines of code. You can also -[build your own custom inference pipleline](../../inference_with_metadata/lite_support) +[build your own custom inference pipeline](../../inference_with_metadata/lite_support) using the TensorFlow Lite Support Library. The Android example below demonstrates the implementation for both methods as