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bird_bar [2026/02/07 22:59] – ↷ Links adapted because of a move operation qlyoungbird_bar [2026/02/08 00:58] (current) – update history section qlyoung
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 At the start of 2021 I received a window-mount bird feeder as a secret santa gift. As a bird lover I was excited to put it up and get a close up view of some of the birds that inhabited the woods around where I lived. Within around 3 days I had birds showing up regularly. At the start of 2021 I received a window-mount bird feeder as a secret santa gift. As a bird lover I was excited to put it up and get a close up view of some of the birds that inhabited the woods around where I lived. Within around 3 days I had birds showing up regularly.
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 +With the floor plan of my apartment at the time, the only sensible place to put the feeder was on the kitchen window; there was a screened porch on my bedroom window, or I would have put it there. Since my work desk was in my bedroom, this meant that I couldn't watch it while I worked. If that had been an option, the rest of the project may never have materialized.
  
 Shortly after installing the feeder I had the idea to mount a camera pointing at it and stream it to Twitch, so that I could watch the birds while I was at my computer in another room. While watching I found myself wondering about a few of the species I saw and looking up pictures trying to identify them. Then it hit me - this is a textbook computer vision problem. I could build something that used realtime computer vision to identify birds as they appeared on camera. Shortly after installing the feeder I had the idea to mount a camera pointing at it and stream it to Twitch, so that I could watch the birds while I was at my computer in another room. While watching I found myself wondering about a few of the species I saw and looking up pictures trying to identify them. Then it hit me - this is a textbook computer vision problem. I could build something that used realtime computer vision to identify birds as they appeared on camera.
  
-Fast forward a few years and this has bloomed into a pretty large project. I have gone through three iterations of the project. It's definitely the most popular project I've made; my friends think it's cool.+Fast forward a few years and this has bloomed into a pretty large project, with multiple upgrades to both the hardware, software and feeder setup. It's definitely the most popular project I've made; my friends think it's cool. It's also served as a good test bed to keep up to date on advances in machine learning and accelerated computing.
  
-===== The Feeder =====+===== Feeder =====
  
 This section covers the evolution of the feeder construction & installation details. This section covers the evolution of the feeder construction & installation details.
  
-With the floor plan of my apartment, the only sensible place to put the feeder was on the kitchen window; there’s a screened porch on my bedroom window, or I would have put it there. This meant that I couldn't watch it while I worked since my desk is in another room. If that had been an option, the rest of the project may never have materialized.+==== v1 ====
  
 Initially the feeder was mounted 'stock'. The camera was an old webcam I had lying around. Since it's mounted outside it needed to be weatherproofed. I did that with plastic wrap. Subsequent attempts greatly improved the design. Initially the feeder was mounted 'stock'. The camera was an old webcam I had lying around. Since it's mounted outside it needed to be weatherproofed. I did that with plastic wrap. Subsequent attempts greatly improved the design.
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 ===== Bird Identification ===== ===== Bird Identification =====
  
-{{ :me-with-phone-yolo-detection.png?200|Screen capture of webcam feed after applying YOLOv5's out-of-box 'small' model to a scene of me holding up my cell phone. Picture is heavily blurred}}+Birds arriving at the feeder are identified using [[https://github.com/ultralytics/yolov5|YOLOv5]] fine tuned on [[https://dl.allaboutbirds.org/nabirds|NABirds]]. 
 +==== Background ====
  
 I’d read about [[https://pjreddie.com/darknet/yolo/|YOLO]] some years before and began to reacquaint myself. It’s come quite far and seems to be more or less the state of the art for realtime computer vision object detection and classification. I downloaded the latest version ([[https://github.com/ultralytics/yolov5|YOLOv5]] at time of writing) and ran the webcam demo. It ran well over 30fps with good accuracy on my RTX3080, correctly picking out myself as “person”, my phone as “cell phone”, and my light switch as “clock”. I’d read about [[https://pjreddie.com/darknet/yolo/|YOLO]] some years before and began to reacquaint myself. It’s come quite far and seems to be more or less the state of the art for realtime computer vision object detection and classification. I downloaded the latest version ([[https://github.com/ultralytics/yolov5|YOLOv5]] at time of writing) and ran the webcam demo. It ran well over 30fps with good accuracy on my RTX3080, correctly picking out myself as “person”, my phone as “cell phone”, and my light switch as “clock”.
 +
 +{{:me-with-phone-yolo-detection.png?400|Screen capture of webcam feed after applying YOLOv5's out-of-box 'small' model to a scene of me holding up my cell phone. Picture is heavily blurred}}
  
 Out of the box YOLOv5 is trained on COCO, which is a dataset of _co_mmon objects in _co_ntext. This dataset is able to identify a picture of a Carolina chickadee as “bird”. Tufted titmice are also identified as “bird”. All birds are “bird” to COCO (at least the ones I tried). Out of the box YOLOv5 is trained on COCO, which is a dataset of _co_mmon objects in _co_ntext. This dataset is able to identify a picture of a Carolina chickadee as “bird”. Tufted titmice are also identified as “bird”. All birds are “bird” to COCO (at least the ones I tried).
  
-{{:chickadee-2.jpg?200|Image of chickadee with a poorly sized bounding box drawn around it with the label "bird" and a confidence rating of 0.31}}+{{:chickadee-2.jpg?400|Image of chickadee with a poorly sized bounding box drawn around it with the label "bird" and a confidence rating of 0.31}}
  
 Pretty good, but not exactly what I was going for. YOLO needed to be trained to recognize specific bird species. Pretty good, but not exactly what I was going for. YOLO needed to be trained to recognize specific bird species.
  
-===== Dataset =====+==== Dataset ====
  
 A quick Google search for “north american birds dataset” yielded probably the most convenient dataset I could possibly have asked for. Behold, [[https://dl.allaboutbirds.org/nabirds|NABirds]]! A quick Google search for “north american birds dataset” yielded probably the most convenient dataset I could possibly have asked for. Behold, [[https://dl.allaboutbirds.org/nabirds|NABirds]]!
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