To solve the above issues, we collect a large-scale object localization and counting dataset at 28 different stores and apartments, which consists of 50,394 images with the JPEG image resolution of 1920x1080 pixels. More than 1.9 million object instances in 140 categories (including *Jacket*, *Shoes*, *Oven*, etc.) are annotated.
To solve the above issues, we collect a large-scale object localization and counting dataset at 28 different stores and apartments, which consists of 50,394 images with the JPEG image resolution of 1920x1080 pixels.
More than 1.9 million object instances in 140 categories (including *Jacket*, *Shoes*, *Oven*, etc.) are annotated.
To facilitate data usage, we divide the dataset into two subsets, i.e., *training* and *testing* sets, including 34,022 images for training and 16,372 images for testing.
To facilitate data usage, we divide the dataset into two subsets, i.e., *training* and *testing* sets, including 34,022 images for training and 16,372 images for testing.
The dataset includes 9 big subclasses, i.e., Baby Stuffs (e.g., *Baby Diapers* and *Baby Slippers*), Drinks (e.g., *Juice* and *Ginger Tea*), Food Stuff (e.g., *Dried Fish* and *Cake*), Daily Chemicals (e.g., *Soap* and *Shampoo*), Clothing (e.g., *Jacket* and *Adult hats*),
As mentioned above, we acquire the dataset at $28$ different stores and apartments. The dataset contains 140 common commodities, including 9 big subclasses, i.e., Baby Stuffs (e.g., *Baby Diapers* and *Baby Slippers*), Drinks (e.g., *Juice* and *Ginger Tea*), Food Stuff (e.g., *Dried Fish* and *Cake*), Daily Chemicals (e.g., *Soap* and *Shampoo*), Clothing (e.g., *Jacket* and *Adult hats*),
Electrical Appliances (e.g., *Microwave Oven* and *Socket*), Storage Appliances (e.g., *Trash* and *Stool*), Kitchen Utensils (e.g., *Forks* and *Food Box*), and Stationery and Sporting Goods (e.g., *Skate* and *Notebook*).
Electrical Appliances (e.g., *Microwave Oven* and *Socket*), Storage Appliances (e.g., *Trash* and *Stool*), Kitchen Utensils (e.g., *Forks* and *Food Box*), and Stationery and Sporting Goods (e.g., *Skate* and *Notebook*).
There are various factors challenging the performance of algorithms, such as scale changes, illumination variations, occlusion, similar appearance, clutter background, blurring and deformation, *etc*.
There are various factors challenging the performance of algorithms, such as scale changes, illumination variations, occlusion, similar appearance, clutter background, blurring and deformation, *etc*.
