Update TFLite Docs images (#8605)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/datasets/detect/roboflow-100.md

@@ -14,7 +14,7 @@ Roboflow 100, developed by [Roboflow](https://roboflow.com/?ref=ultralytics) and
 
 ## Key Features
 
-- Includes 100 datasets across seven domains: Aerial, Videogames, Microscopic, Underwater, Documents, Electromagnetic, and Real World.
+- Includes 100 datasets across seven domains: Aerial, Video games, Microscopic, Underwater, Documents, Electromagnetic, and Real World.
 - The benchmark comprises 224,714 images across 805 classes, thanks to over 11,170 hours of labeling efforts.
 - All images are resized to 640x640 pixels, with a focus on eliminating class ambiguity and filtering out underrepresented classes.
 - Annotations include bounding boxes for objects, making it suitable for [training](../../modes/train.md) and evaluating object detection models.
@@ -24,7 +24,7 @@ Roboflow 100, developed by [Roboflow](https://roboflow.com/?ref=ultralytics) and
 The Roboflow 100 dataset is organized into seven categories, each with a distinct set of datasets, images, and classes:
 
 - **Aerial**: Consists of 7 datasets with a total of 9,683 images, covering 24 distinct classes.
-- **Videogames**: Includes 7 datasets, featuring 11,579 images across 88 classes.
+- **Video Games**: Includes 7 datasets, featuring 11,579 images across 88 classes.
 - **Microscopic**: Comprises 11 datasets with 13,378 images, spanning 28 classes.
 - **Underwater**: Contains 5 datasets, encompassing 18,003 images in 39 classes.
 - **Documents**: Consists of 8 datasets with 24,813 images, divided into 90 classes.
@@ -45,7 +45,7 @@ For more ideas and inspiration on real-world applications, be sure to check out
 
 ## Usage
 
-The Roboflow 100 dataset is available on both [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100). 
+The Roboflow 100 dataset is available on both [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100).
 
 You can access it directly from the Roboflow 100 GitHub repository. In addition, on Roboflow Universe, you have the flexibility to download individual datasets by simply clicking the export button within each dataset.
 

docs/en/datasets/explorer/api.md

@@ -227,7 +227,7 @@ Here are some examples of what you can do with the table:
     print(embeddings)
     ```
 
-### Advanced Querying with pre and post filters
+### Advanced Querying with pre- and post-filters
 
 !!! Example
 

docs/en/datasets/explorer/dashboard.md

@@ -39,7 +39,7 @@ pip install ultralytics[explorer]
 Semantic search is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar embeddings. In the UI, you can select one of more images and search for the images similar to them. This can be useful when you want to find images similar to a given image or a set of images that don't perform as expected.
 
 For example:
-In this VOC Exploration dashboard, user selects a couple aeroplane images like this:
+In this VOC Exploration dashboard, user selects a couple airplane images like this:
 <p>
 <img width="1710" alt="Explorer Dashboard Screenshot 2" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/3becdc1d-45dc-43b7-88ff-84ff0b443894">
 </p>

docs/en/datasets/index.md

@@ -34,7 +34,7 @@ Bounding box object detection is a computer vision technique that involves detec
 - [VOC](detect/voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
 - [xView](detect/xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
 - [Roboflow 100](detect/roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
-  
+
 ## [Instance Segmentation Datasets](segment/index.md)
 
 Instance segmentation is a computer vision technique that involves identifying and localizing objects in an image at the pixel level.

docs/en/datasets/obb/dota-v2.md

@@ -68,7 +68,7 @@ Typically, datasets incorporate a YAML (Yet Another Markup Language) file detail
 
 ## Split DOTA images
 
-To train DOTA dataset, We split original DOTA images with high-resolution into images with 1024x1024 resolution in multi-scale way.
+To train DOTA dataset, we split original DOTA images with high-resolution into images with 1024x1024 resolution in multiscale way.
 
 !!! Example "Split images"