Expand COCO classroom example bank to 24 images

.gitattributes

@@ -50,3 +50,18 @@ examples/000000397133.jpg filter=lfs diff=lfs merge=lfs -text
 guide-assets/duplicate-menu.png filter=lfs diff=lfs merge=lfs -text
 guide-assets/space-home.png filter=lfs diff=lfs merge=lfs -text
 how-to-start.pdf filter=lfs diff=lfs merge=lfs -text
+examples/000000010092.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000063965.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000090208.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000097278.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000189806.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000199442.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000210520.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000286458.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000365886.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000429281.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000431876.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000482978.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000500270.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000517523.jpg filter=lfs diff=lfs merge=lfs -text
+examples/000000570448.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -28,7 +28,7 @@ The point of the project is not to make students rebuild an object detector from
 - Additional browser comparison models: `Xenova/detr-resnet-50`, `Xenova/yolos-tiny`
 - Task: object detection
 - Training dataset: COCO
-- Example bank: selected images from COCO 2017 validation
+- Example bank: 24 selected images from COCO 2017 validation
 
 All three browser models are already trained. This demo performs inference only; no model training or GPU server is required.
 

app.js

@@ -32,6 +32,22 @@ const EXAMPLES = [
   { src: './examples/000000174482.jpg', label: 'Bike & traffic' },
   { src: './examples/000000181666.jpg', label: 'Herd & person' },
   { src: './examples/000000376310.jpg', label: 'Bathroom objects' },
+  { src: './examples/000000010092.jpg', label: 'Bedroom furniture' },
+  { src: './examples/000000063965.jpg', label: 'Cake & person' },
+  { src: './examples/000000090208.jpg', label: 'Double-decker buses' },
+  { src: './examples/000000097278.jpg', label: 'Snowboarder' },
+  { src: './examples/000000167898.jpg', label: 'Bathroom sink & toilet' },
+  { src: './examples/000000189806.jpg', label: 'Cat & dog at home' },
+  { src: './examples/000000199442.jpg', label: 'Surfer' },
+  { src: './examples/000000210520.jpg', label: 'Dining table' },
+  { src: './examples/000000286458.jpg', label: 'Packed suitcase' },
+  { src: './examples/000000365886.jpg', label: 'Person & cell phone' },
+  { src: './examples/000000429281.jpg', label: 'Bananas at market' },
+  { src: './examples/000000431876.jpg', label: 'Horses & riders' },
+  { src: './examples/000000482978.jpg', label: 'Passenger train' },
+  { src: './examples/000000500270.jpg', label: 'Skateboarder' },
+  { src: './examples/000000517523.jpg', label: 'Airplane landing' },
+  { src: './examples/000000570448.jpg', label: 'Boat at the beach' },
 ];
 
 function chooseRandom(items, count) {

examples/coco_val2017_subset.csv

@@ -7,3 +7,19 @@ image_id,classroom_label,source_url
 000000174482,Bike & traffic,http://images.cocodataset.org/val2017/000000174482.jpg
 000000181666,Herd & person,http://images.cocodataset.org/val2017/000000181666.jpg
 000000376310,Bathroom objects,http://images.cocodataset.org/val2017/000000376310.jpg
+000000010092,Bedroom furniture,http://images.cocodataset.org/val2017/000000010092.jpg
+000000063965,Cake & person,http://images.cocodataset.org/val2017/000000063965.jpg
+000000090208,Double-decker buses,http://images.cocodataset.org/val2017/000000090208.jpg
+000000097278,Snowboarder,http://images.cocodataset.org/val2017/000000097278.jpg
+000000167898,Bathroom sink & toilet,http://images.cocodataset.org/val2017/000000167898.jpg
+000000189806,Cat & dog at home,http://images.cocodataset.org/val2017/000000189806.jpg
+000000199442,Surfer,http://images.cocodataset.org/val2017/000000199442.jpg
+000000210520,Dining table,http://images.cocodataset.org/val2017/000000210520.jpg
+000000286458,Packed suitcase,http://images.cocodataset.org/val2017/000000286458.jpg
+000000365886,Person & cell phone,http://images.cocodataset.org/val2017/000000365886.jpg
+000000429281,Bananas at market,http://images.cocodataset.org/val2017/000000429281.jpg
+000000431876,Horses & riders,http://images.cocodataset.org/val2017/000000431876.jpg
+000000482978,Passenger train,http://images.cocodataset.org/val2017/000000482978.jpg
+000000500270,Skateboarder,http://images.cocodataset.org/val2017/000000500270.jpg
+000000517523,Airplane landing,http://images.cocodataset.org/val2017/000000517523.jpg
+000000570448,Boat at the beach,http://images.cocodataset.org/val2017/000000570448.jpg

index.html

@@ -114,7 +114,7 @@
 
             <p class="helper">
               Four images are drawn at random
-              from our pre-tested COCO sample bank.
+              from 24 bundled COCO validation examples.
             </p>
           </div>