Indoor & Outdoor Scene Segmentation

@hotanloc

I have strong experience in Image Segmentation and Computer Vision using deep learning frameworks. I have already completed a similar computer vision project successfully. For your project, I can develop an accurate image segmentation model to identify and separate objects from images or video frames. My approach will include: • Preparing and annotating the dataset (if needed) • Training a segmentation model (U-Net, Mask R-CNN, or YOLO segmentation) • Optimizing the model for high accuracy and performance • Implementing the segmentation pipeline using OpenCV / Python • Delivering clean and well-documented code My technical expertise includes: • Python • OpenCV • PyTorch / TensorFlow / Keras • YOLO / Mask R-CNN / U-Net • Image processing and deep learning I have experience working with CCTV video analysis, object detection, tracking, counting, and segmentation, and I can deliver a reliable and efficient solution. I am confident I can complete this project with high quality and within a short timeframe. Please feel free to message me to discuss the project details. Thank you.

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe) & I can deliver your indoor+outdoor semantic segmentation dataset + trained model + fully reproducible pipeline, while reducing manual labeling via assisted auto-annotation (SAM2 / Grounded-SAM + CVAT QA) so quality stays high Relevant work: >>Built production computer-vision pipelines end-to-end (detection/segmentation + embedding generation), deployed on AWS (Lambda/SQS/S3) with reproducible Docker workflows >>Designed large-scale annotation + evaluation loops: auto-prelabels -> human QA -> active-learning retrain, with strict class-mapping rules to keep labels consistent >>Shipped search + metadata systems where accuracy mattered: hybrid CV/NLP pipelines combining segmentation-like masks/regions, embeddings & structured storage (PostgreSQL/Elasticsearch) with cosine-similarity retrieval >>Worked heavily on robust image preprocessing (RAW/JPEG handling, resizing, EXIF extraction)& model monitoring/debugging for real-world noisy media. Approach: 1. Define class taxonomy + mapping (road/café) + ignore rules 2. Generate strong pseudo-masks using road priors + open-vocab prompting (SAM2/GroundingDINO) & import into CVAT for fast correction 3. Train SegFormer/MMSegmentation on Ubuntu 22.04 + CUDA, track mIoU per-class, iterate via active learning until your target (≥0.75 mIoU) on a private split 4. Deliver clean repo: scripts for dataset prep, training, inference & exportable model checkpoints

@Techroj

Hi, I’d be happy to help develop a complete semantic segmentation pipeline for this dataset. I have professional experience as a freelancer working with computer vision, deep learning models, and dataset preparation using frameworks like PyTorch and modern segmentation architectures. I can handle high-quality annotation workflows using tools such as CVAT or LabelMe and structure the dataset so it remains clean and easy to maintain. For the model, I would implement a robust training pipeline using architectures like DeepLabV3+ or SegFormer with CUDA acceleration, including evaluation metrics such as mIoU and clear experiment tracking. The final delivery will include reproducible training scripts, inference utilities, and detailed documentation for Ubuntu environments. I’d be glad to share examples and discuss the approach over DMs. With regards, Rojan Uprety

@ABDOKAMA

Hi, I have extensive experience in Computer Vision and have successfully delivered several projects similar to yours. My previous work includes developing models for facial hair detection and animal identification in Nagarahole National Park. I am confident that my expertise in semantic segmentation and object detection will allow me to deliver a high-quality solution for your indoor and outdoor scene analysis.

@hassam945

Hi, your work is exactly in my domain. You can check my profile and portfolio my past projects clearly reflect why I’m a strong fit for this. I have solid experience in semantic segmentation for real-world datasets (road + mixed environments), including annotation, training, and optimization. I will deliver high-quality pixel-level masks using tools like CVAT/LabelMe with a clean class structure, and build a reproducible pipeline using PyTorch (DeepLabV3+/SegFormer). The training setup will be optimized to achieve ≥0.75 mIoU with proper augmentation, validation split, and tuning. You will get clean code, README, and an easy-to-run workflow on Ubuntu (CUDA ready). I can also share a quick demo/sample before we proceed so you can verify quality. "I prefer to discuss first because we both need to understand first each other then if its align we start working . So its not a complex for me . Last thing , I am not here for juggling clients you visit my freelancer profile it gives you clarity about all things you need .I am confident about that thing I deliver your work with requirement satisfaction and Clarity. I am new here but have enough experience to provide quality clean work to you. Lets connect I promise you will never disappointed .Thank you

@PyMLExpert1

With my extensive experience in Computer Vision and Deep Learning, I can confidently take on your Indoor & Outdoor Scene Segmentation project. I am well-versed in using various frameworks like PyTorch, TensorFlow, Detectron2 and more, all of which align perfectly with the flexible workflow you desire. Additionally, my robust understanding of managing large-scale datasets will ensure the preparation, annotation, training, inference pipelines run seamlessly on Ubuntu 22.04 with CUDA. Notably, I have developed models capable of achieving impressive 0.85+ mIoU scores on diverse segmentation tasks and can develop a similar solution tailored specifically for your needs. What sets me apart is not just my technical expertise but my penchant for creating end-to-end solutions that are both efficient and easy-to-deploy. I will provide you with meticulously annotated high-quality masks for all your indoor and outdoor images matched to your class list. Moreover, my thorough README documentation will enable you to comfortably make modifications as per your requirements. Given the nature of your data being a mixed-collection, my experience handling multi-domain training sets makes me an ideal fit for this project. This has prepared me to tackle diverse challenges similar to those you have outlined, and even though most of your data is outdoors-focused, I assure you those café frames will be handled brilliantly too.

@ffulb

Your mixed indoor/outdoor dataset with café scenes plus road elements is an interesting segmentation challenge. I'll use DeepLabV3+ or SegFormer as the backbone, create high-quality pixel masks in CVAT covering your class list from road/sidewalk to café tables/chairs, then build a complete PyTorch training pipeline targeting your 0.75 mIoU requirement. I've handled similar computer vision work with my price aggregation engine that processes thousands of product images daily for classification and my content automation system that analyzes visual content across multiple domains. You can see my technical work at ffulb.com. Can start immediately and deliver the annotated dataset, trained model, and reproducible code within your timeline. The workflow will be modular so you can easily adjust classes or retrain later.

@joep84

Your project caught my eye because it sits at an interesting intersection — mixed-domain segmentation where the model needs to generalize across outdoor road scenes and indoor café environments. That's not a trivial annotation challenge, and getting the class taxonomy right across both domains is half the battle. Here's how I'd approach this: I'd start with DeepLabV3+ or SegFormer as the backbone, since both handle multi-class semantic segmentation well and are straightforward to fine-tune on custom datasets. For annotation, CVAT is my go-to — it supports polygon and brush-based labeling, exports directly to COCO format, and makes iterating on the class list painless. Since your outdoor scenes likely overlap with Cityscapes-style classes, I'd leverage pretrained weights from that domain and fine-tune on your specific data, which should give us a significant head start toward hitting that 0.75 mIoU target even with a modest dataset size. For the pipeline, I'd build it in PyTorch with a clean train/val/test split workflow, config-driven so you can swap backbones or adjust hyperparameters without touching the training loop. Everything containerized or at minimum reproducible on Ubuntu 22.04 + CUDA with a requirements file and a step-by-step README. I'm comfortable with the full stack here — Python, CUDA environments, Linux, Docker — so you'll get something that actually runs on your machine, not just on mine. I'm happy to annotate a small batch of your images first and train a quick baseline so you can evaluate quality before committing to the full scope. That way you see real output, not just promises. Drop me a message and we can sort out the class list and get a sample segment running within a day or two.

@Dongfreelancer

Hi! I am very interested in your scene segmentation project. I have a sharp eye for detail and can accurately distinguish between various indoor and outdoor elements. I am committed to following your annotation guidelines strictly to ensure high-quality data for your model. I am hardworking, reliable, and ready to start immediately. Looking forward to working with you. Thank you!