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Top Ice Cream Computer Vision Models
The models below have been fine-tuned for various ice cream detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own ice cream detection model.
At the bottom of this page, we have guides on how to count ice cream in images and videos.
172 images2 models
BUD LIGHT CHELADA MANGONADA 1/25C Electrolit Strawberry Banana 21oz Monster Energy Drink Juice Aussie Style Lemonade 16oz Red Bull Amber Edition Strawberry Apricot 8.4oz Simply Orange with Mango 11.5oz7S Alkaline Water 1L7S Farmers Grove Apple Juice 117S Farmers Grove Apple Juice 11.5oz7S Farmers Grove Cranberry 117S Farmers Grove Cranberry 11.5oz7S Farmers Grove Lemonade 117S Farmers Grove Lemonade 11.5oz7S Farmers Grove Orange Juice No Pulp 117S Farmers Grove Orange Juice No Pulp 11.5oz7S Farmers Grove Raspberry Lemonade 117S Farmers Grove Raspberry Lemonade 11.5oz7S Iced Cappuccino Mocha 12oz7S Iced Cappuccino Salted Caramel 12oz7S Iced Cappuccino Vanilla 12oz7S Organic Cold Pressed Berry Banana Bliss 14oz
4800 images1 model
869 images1 model
cardboard boxplastic cupALDIPlastic BagAfiaAfia Plastic BottleAlliance Lysol Plastic JerrycanAmigos Plastic BagAmplico Dawa Plastic BottleAquaAquaclearAquafrostAriel Plastic SacheAssured Plastic ContainerAzam Energy DrinkAzam Energy Plastic BottleBazuu Energy DrinkBazuu Energy Plastic BottleBella Plastic BagBlueband Plastic sacheBrava
292 images1 model
783 images1 model
cardboard boxplastic cupALDIPlastic BagAfia Plastic BottleAlliance Lysol Plastic JerrycanAmigos Plastic BagAmplico Dawa Plastic BottleAriel Plastic SacheAssured Plastic ContainerAzam Energy Plastic BottleBazuu Energy Plastic BottleBella Plastic BagBlueband Plastic sacheBrookSide Lala Plastic BottleButterfly Plastic BagCablesCardboard CartonCeramic mugCeramic mugsChandaranaPlastic Shopping Bag
783 images1 model
cardboard boxplastic cupALDIPlastic BagAfia Plastic BottleAlliance Lysol Plastic JerrycanAmigos Plastic BagAmplico Dawa Plastic BottleAriel Plastic SacheAssured Plastic ContainerAzam Energy Plastic BottleBazuu Energy Plastic BottleBella Plastic BagBlueband Plastic sacheBrookSide Lala Plastic BottleButterfly Plastic BagCablesCardboard CartonCeramic mugCeramic mugsChandaranaPlastic Shopping Bag
314 images1 model
Aer Matic Violet Valley Bloom Automatic Spray Diffuser Refill 225 mlAmul ButterAmul Ice Cream - Vanilla Magic 1 L TubBadshah Afgani HingBaygon Mosquito & Fly Killer Spray 625 mlBisleri Club Soda - 750 mlBisleri Packaged Drinking Water 250 mlBisleri Packaged Drinking Water 500 mlBrooke Bond Red Label Leaf Tea 500 gCadbury Bournvita Chocolate Health Drink(1kg)Catch Amchur Powder 100 gCatch Ginger Garlic Paste 200 gChing's secret schezwan fried masalaChings Secret Veg Hakka Noodles 140 g PouchCoca-Cola(1 liter)Coca-Cola(600 ml)Dabur Dant Rakshak Ayurvedic Paste 175 g (Pack of 2)Dettol Bathing Soap Bar - Original 125gDettol Original Instant Hand Sanitizer 50 mlEverest Chaat Masala 50 g
931 images1 model
cardboard boxplastic cupALDIPlastic BagAfia Plastic BottleAlliance Lysol Plastic JerrycanAmigos Plastic BagAmplico Dawa Plastic BottleAriel Plastic SacheAssured Plastic ContainerAzam Energy Plastic BottleBazuu Energy Plastic BottleBella Plastic BagBlueband Plastic sacheBrookSide Lala Plastic BottleButterfly Plastic BagCablesCardboard CartonCeramic mugCeramic mugsChandaranaPlastic Shopping Bag
Guide: How to Count Ice Cream with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count ice cream in your images and videos.
The following code snippet counts the number of ice cream present in an image.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import supervision as sv
import roboflow
roboflow.login()
rf = roboflow.Roboflow()
# replace with the ice cream project you choose above
project = rf.workspace("ai-nutrient-tracker").project("object-detection-ez3ce")
ice_cream_model = project.version(1).model
results = ice_cream_model.predict("ice_cream.jpg").json()
ice_cream = sv.Detections.from_roboflow(results)
# print number of ice cream
print(len(ice_cream))Guide: How to Count Ice Cream in a Zone
With a bit more code, you can count the number of ice cream present in a specific zone of your image or video.
The following code snippet counts the number of ice cream present in each frame in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
Read our blog post on counting objects in a zone
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "ice_cream.mp4"
TARGET_VIDEO_PATH = "ice_cream_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your shape
polygon = np.array([
# draw 50x50 box in top left corner
[0, 0],
[50, 0],
[50, 50],
[0, 50]
])
roboflow.login()
rf = roboflow.Roboflow()
# replace with the ice cream project you choose above
project = rf.workspace("ai-nutrient-tracker").project("object-detection-ez3ce")
ice_cream_model = project.version(1).model
# create BYTETracker instance
ice_cream_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create PolygonZone instance
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=(video_info.width, video_info.height))
# create box annotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
colors = sv.ColorPalette.default()
# create instance of BoxAnnotator
zone_annotator = sv.PolygonZoneAnnotator(thickness=4, text_thickness=4, text_scale=2, zone=zone, color=colors.colors[0])
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = ice_cream_model.predict(frame).json()
ice_cream = sv.Detections.from_roboflow(results)
# show ice cream detections in real time
print(ice_cream)
# tracking ice cream detections
ice_cream = ice_cream_tracker.update_with_detections(ice_cream)
annotated_frame = box_annotator.annotate(scene=frame, detections=ice_cream)
annotated_frame = zone_annotator.annotate(scene=annotated_frame)
# return frame with box and line annotated result
return annotated_frame
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)Guide: How to Track Ice Cream Crossing a Line
You can count how many ice cream have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of ice cream that cross a line in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "ice_cream.mp4"
TARGET_VIDEO_PATH = "ice_cream_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your line
LINE_START = sv.Point(0, 300)
LINE_END = sv.Point(800, 300)
roboflow.login()
rf = roboflow.Roboflow()
# replace with the ice cream project you choose above
project = rf.workspace("ai-nutrient-tracker").project("object-detection-ez3ce")
ice_cream_model = project.version(1).model
# create BYTETracker instance
ice_cream_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create LineZone instance, it is previously called LineCounter class
line_zone = sv.LineZone(start=LINE_START, end=LINE_END)
# create instance of BoxAnnotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
# create instance of TraceAnnotator
trace_annotator = sv.TraceAnnotator(thickness=4, trace_length=50)
line_zone_annotator = sv.LineZoneAnnotator(thickness=4, text_thickness=4, text_scale=2)
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = ice_cream_model.predict(frame).json()
ice_cream = sv.Detections.from_roboflow(results)
# show ice cream detections in real time
print(ice_cream)
# tracking ice cream detections
ice_cream = ice_cream_tracker.update_with_detections(ice_cream)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=ice_cream
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=ice_cream
)
# update line counter
line_zone.trigger(ice_cream)
# return frame with box and line annotated result
return line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)