Related Objects of Interest: car , truck , pedestrian , person , van , misc , tram , person_sitting , bus , dontcare
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Top Cyclist Computer Vision Models

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

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

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Guide: How to Count Cyclists with Computer Vision

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

The following code snippet counts the number of cyclists 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 cyclist project you choose above
            project = rf.workspace("leo-ueno").project("people-detection-o4rdr")
            cyclist_model = project.version(9).model
            
            results = cyclist_model.predict("cyclist.jpg").json()
            cyclists = sv.Detections.from_roboflow(results)
            
            # print number of cyclists
            print(len(cyclists))

Guide: How to Count Cyclists in a Zone

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

The following code snippet counts the number of cyclist 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 = "cyclist.mp4"
            TARGET_VIDEO_PATH = "cyclist_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 cyclist project you choose above
            project = rf.workspace("leo-ueno").project("people-detection-o4rdr")
            cyclist_model = project.version(9).model
            
            # create BYTETracker instance
            cyclist_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 = cyclist_model.predict(frame).json()
                cyclists = sv.Detections.from_roboflow(results)
            
                # show cyclist detections in real time
                print(cyclists)
            
                # tracking cyclist detections
                cyclists = cyclist_tracker.update_with_detections(cyclists)
            
                annotated_frame = box_annotator.annotate(scene=frame, detections=cyclists)
                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 Cyclists Crossing a Line

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

The following code snippet counts the number of cyclists 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 = "cyclist.mp4"
            TARGET_VIDEO_PATH = "cyclist_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 cyclist project you choose above
            project = rf.workspace("leo-ueno").project("people-detection-o4rdr")
            cyclist_model = project.version(9).model
            
            # create BYTETracker instance
            cyclist_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 = cyclist_model.predict(frame).json()
                cyclists = sv.Detections.from_roboflow(results)
            
                # show cyclist detections in real time
                print(cyclists)
            
                # tracking cyclist detections
                cyclists = cyclist_tracker.update_with_detections(cyclists)
                annotated_frame = trace_annotator.annotate(
                    scene=frame.copy(),
                    detections=cyclists
                )
                annotated_frame=box_annotator.annotate(
                    scene=annotated_frame,
                    detections=cyclists
                )
            
                # update line counter
                line_zone.trigger(cyclists)
            
                # 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
            )