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I am assembling a diverse, real-world image dataset to sharpen an object-detection AI and I need fresh photographs from Zone 1 captured and delivered as soon as possible. Your task is straightforward: shoot authentic urban streetscapes, natural outdoor scenes, and everyday indoor environments in your area, then supply the original, unedited files plus basic metadata (location city, date, time, and the main objects visible). To be accepted, every image must • be your own recent photo, not scraped or stock • arrive in high-resolution JPEG or RAW with intact EXIF • respect local privacy laws (no identifiable faces as the focus) Place all images in clearly named folders for Urban, Natural, and Indoor and transfer them via a shared cloud link once complete. Speed matters ...

Here’s a Freelancer project post focused on the bird-versus-drone problem: **Title: Improve Image-Only Drone Detection to Reduce Bird False Positives on Raspberry Pi + Hailo-10H** I have an existing Raspberry Pi camera pipeline running a single-class (“Drone”) YOLO model on a Hailo-10H. Birds, especially at a distance, may be detected as drones. I need a computer-vision engineer to develop and evaluate an image-only solution that reduces these false positives without losing too many real drone detections. The camera is fixed to the drone body. The current model is available as a compiled HEF; availability of its original training weights and dataset needs to be confirmed. Development must go in a new `DetectionV2` folder. Existing Detection, camera, telemetry, and inte...

I have a fully-labeled dataset containing vehicles and their licence plates and now need it to power a production-ready object-detection model. I’m flexible on the backbone—you can work with YOLO, SSD, Faster R-CNN or whichever modern architecture you feel will squeeze out the best accuracy—but the end result must reliably spot both the vehicle and the plate in a single pass. What I will provide • Training, validation and test splits already annotated in COCO-style JSON • A small script that shows the folder layout I use for images and labels What I expect back • The fine-tuned weights • All training and evaluation scripts (Python preferred; PyTorch or TensorFlow are both fine) • A quick-start readme so I can reproduce your results o...

I need to fine-tune an existing computer-vision model for object detection so it performs reliably on my custom dataset. The base architecture is already selected; the task now is to prepare the data, set up transfer-learning, run hyper-parameter sweeps, and deliver a set of weights that reach an agreed-upon mAP on a held-out validation split. You may use PyTorch, TensorFlow, or another mainstream framework as long as the final model can be exported to ONNX. Please share examples of past work where you have successfully fine-tuned or trained object-detection networks—links to repos, papers, or short demos are ideal. Once we decide on the starting checkpoint and training environment, I’ll supply labeled images, class definitions, and any additional annotations you need. Delive...