Related Objects of Interest: banana , apple , kiwi , pineapple , orange , tomato , cucumber , peach , mango , pear
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Top Cherry Computer Vision Models

The models below have been fine-tuned for various cherry 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 cherry detection model.

At the bottom of this page, we have guides on how to count cherries in images and videos.

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Guide: How to Count Cherries with Computer Vision

With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count cherries in your images and videos.

The following code snippet counts the number of cherries 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 cherry project you choose above
            project = rf.workspace("ai-nutrient-tracker").project("object-detection-ez3ce")
            cherry_model = project.version(1).model
            
            results = cherry_model.predict("cherry.jpg").json()
            cherries = sv.Detections.from_roboflow(results)
            
            # print number of cherries
            print(len(cherries))

Guide: How to Count Cherries in a Zone

With a bit more code, you can count the number of cherry present in a specific zone of your image or video.

The following code snippet counts the number of cherry 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 = "cherry.mp4"
            TARGET_VIDEO_PATH = "cherry_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 cherry project you choose above
            project = rf.workspace("ai-nutrient-tracker").project("object-detection-ez3ce")
            cherry_model = project.version(1).model
            
            # create BYTETracker instance
            cherry_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 = cherry_model.predict(frame).json()
                cherries = sv.Detections.from_roboflow(results)
            
                # show cherry detections in real time
                print(cherries)
            
                # tracking cherry detections
                cherries = cherry_tracker.update_with_detections(cherries)
            
                annotated_frame = box_annotator.annotate(scene=frame, detections=cherries)
                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 Cherries Crossing a Line

You can count how many cherries have crossed a line using the supervision LineCounter method.

The following code snippet counts the number of cherries 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 = "cherry.mp4"
            TARGET_VIDEO_PATH = "cherry_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 cherry project you choose above
            project = rf.workspace("ai-nutrient-tracker").project("object-detection-ez3ce")
            cherry_model = project.version(1).model
            
            # create BYTETracker instance
            cherry_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 = cherry_model.predict(frame).json()
                cherries = sv.Detections.from_roboflow(results)
            
                # show cherry detections in real time
                print(cherries)
            
                # tracking cherry detections
                cherries = cherry_tracker.update_with_detections(cherries)
                annotated_frame = trace_annotator.annotate(
                    scene=frame.copy(),
                    detections=cherries
                )
                annotated_frame=box_annotator.annotate(
                    scene=annotated_frame,
                    detections=cherries
                )
            
                # update line counter
                line_zone.trigger(cherries)
            
                # 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
            )