Product Image Annotation Needed

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@anshumanvermaUK

have experience with image annotation and can follow detailed labeling guidelines carefully. I’d prefer CVAT because it is easy to manage precise boxes/polygons and export in COCO or Pascal VOC format. For 100 product images, I can usually complete the first batch in 1 days, depending on image complexity and the number of objects per image. I can also handle the sentiment labels and keep both datasets consistent. Rate: $11/hour. I’m happy to start with the pilot batch first. Thanks Anshuman

@C0RETECHIES

Hello, System understanding: You need a reliable annotation pipeline for product images-precise bounding boxes or polygons, correct class labels, occlusion/quality flags-plus sentiment tagging for accompanying customer snippets. The workflow involves iterating over images, applying guidelines, exporting COCO JSON or Pascal VOC, and labeling text snippets as positive/neutral/negative, all while maintaining clear communication and hitting deadlines. Technical approach: Use CVAT for its open-source flexibility and support for COCO/VOC outputs, integrating a simple Python script to run sentiment analysis via a lightweight transformer model (e.g., DistilBERT) on text snippets. Store annotations in a structured folder, validate with a QA script, and deliver batches incrementally. Core modules: Image annotation UI (CVAT), label export converter, sentiment analysis module, quality‑check script, batch packaging and delivery. Relevant systems: None directly matching, but we have built NLP pipelines for sentiment tagging and automated data‑processing workflows with n8n. Implementation strategy: Start with a pilot of 10 images to confirm label format and sentiment output, then scale to full set in daily batches, performing QA after each batch and adjusting guidelines as needed. Questions: 1. Which export format-COCO JSON or Pascal VOC-will you finalize for the image annotations? 2. Do you have a predefined label list and sentiment template, or should we propose a starter set based on the pilot? 3. What is the expected weekly volume after the pilot so we can allocate resources accordingly? Regards, Rohit

@arslanshahid386

Hi, I'm interested in your image annotation project. I have experience with precise bounding boxes and class labeling for datasets. Could you clarify how many images are in the companion set of customer-written content? Looking forward to helping you create a clean dataset for model training.

@longngoc2307

==== Hi - Truong here ==== "PIXEL-ACCURATE PRODUCT IMAGE ANNOTATION" — you need consistent boxes, labels, occlusion flags, and clean training-ready exports. I’d use CVAT because it handles precise boxes/polygons and COCO/VOC exports cleanly. I’d also keep the sentiment labels tied to your exact template and flag unclear cases instead of guessing. For the 100-image pilot, do you already have the annotation guidelines and preferred export format?

@hayat38402

Hello, I am Muhammad Javed, an Electrical Engineer with more than 10 years of experience in data annotation, image labeling, quality control, and technical data processing. I have carefully reviewed your requirements and can provide consistent, pixel-accurate product annotation along with the required sentiment tagging. For image annotation, I prefer CVAT because it provides precise bounding boxes and polygons, strong labeling workflows, quality checks, and reliable export to both COCO JSON and Pascal VOC XML. I will carefully follow your annotation guidelines, maintain consistent class labels, identify occlusions, and flag unclear or low-quality images rather than making assumptions. For the text dataset, I can accurately classify snippets as positive, neutral, or negative according to your provided template. For 100 product images, I can typically complete the initial annotation within 1–2 days, depending on image complexity and the number of objects per image. I am comfortable starting with your pilot batch so you can verify accuracy and consistency before expanding the full dataset. I will maintain an organized file structure, perform a final quality review, and communicate promptly whenever a guideline requires clarification. My focus will be on delivering a clean, reliable dataset that is immediately suitable for model training.

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@SkillCrafts

Your need for precise bounding boxes and class labels on product images, mirroring the accuracy required for robust object detection models, aligns perfectly with my expertise. I've successfully delivered annotated datasets that have directly led to significant improvements in model performance for similar e-commerce clients, ensuring clean and ready-to-train data. My approach will involve using a combination of manual annotation tools like Labelbox or CVAT, chosen based on your preferred workflow and integration capabilities. For each image, I will meticulously draw bounding boxes or polygons, assign accurate product class labels, and consistently flag occlusions or quality issues as per your guidelines. For the customer snippets, I'll apply sentiment analysis tags using established NLP libraries like NLTK or spaCy, ensuring a unified and high-quality dataset. To ensure optimal alignment, could you clarify the preferred output format for the annotated data (e.g., COCO, Pascal VOC)? Also, is there a specific set of product categories you'd like me to prioritize or any initial quality assessment criteria for the images? I'm ready to discuss your project in detail and demonstrate how I can deliver a highly accurate and efficient annotation service.

@abdf2010

Hi, I am a professional web developer and I can do this project "Product Image Annotation Needed", I have 5 years of experience in web development. I have done many projects like this. I can do this job for you. I can start right now. Please contact me. Thanks

@ankits0192

Hi, For model-training data, the priority is consistent annotation quality - not simply completing images quickly. I can follow your guidelines carefully and use the pilot batch to establish the exact labeling rules before scaling. My preferred workflow is **CVAT**, because it provides precise bounding-box/polygon annotation, structured labels and reliable dataset exports such as COCO/Pascal VOC. I can handle: * Pixel-accurate boxes or polygons * Correct product class assignment * Occlusion and image-quality flags * Consistent file organization and required export format * Positive/neutral/negative sentiment labels for the text snippets * Early clarification whenever an annotation guideline is ambiguous * Final quality review before delivery For **100 product images**, I would initially estimate around **1-2 working days**, depending on image complexity and the number of objects per image. I’d confirm the exact pace after the pilot batch. I’m comfortable following detailed annotation instructions and prioritizing accuracy and consistency over rushed output. Could you share the pilot batch and annotation guidelines? Also, approximately how many product objects appear per image? I’m ready to complete the pilot first so you can verify quality and speed before expanding the dataset. Best regards, Ankit

@Abror86

Hi-Abror Here From Uzbekistan. "Accurate Product Image Annotation" - I can deliver consistent image labels using CVAT or Labelbox with precise boxes, polygons, classes, and quality flags. I can annotate product images according to your guidelines, handle occlusions, maintain consistent labels, perform sentiment tagging, and export clean COCO JSON or Pascal VOC files. I will complete a pilot batch first, verify annotation quality, maintain organized exports, and provide a reliable turnaround for the remaining dataset. Would you prefer CVAT for the pilot batch, or do you already have a preferred annotation platform? Looking forward to working with you.

@tyronet2

Your priority is annotation consistency: accurate boxes/polygons, correct classes, and clear handling of occlusions or poor-quality images so the dataset is genuinely training-ready. I’m comfortable with image-processing and data-labeling workflows, and I’d use CVAT for this project because it handles bounding boxes, polygons, attributes, QA review, and COCO/Pascal VOC export cleanly. I’d first annotate a small pilot batch, confirm edge cases against your guideline, then apply the same rules consistently across the full set. I can also label the companion text snippets as positive, neutral, or negative using a fixed rubric so visual and sentiment annotations stay equally reproducible. For 100 moderately complex product images, I’d expect roughly 1–2 days depending on object count and polygon detail. I’d also keep filenames, class IDs, and export structure organized so the dataset can drop directly into training. Are most images single-product shots, or do they contain multiple overlapping products?

@misbahn209

Having experience in image annotation, data labeling, and quality checking. I can accurately create bounding boxes/polygons, apply class labels, flag occlusions and handle basic sentiment tagging while carefully following your guidelines. I prefer CVAT for its accuracy and efficient workflow! I’m happy to start with a pilot batch and ensure clean, consistent, high-quality output.

@glennw6

Hello, I’m a computer vision data annotator experienced with both CVAT and Label Studio, and I’d be happy to help create a clean, reliable dataset for your project. I can handle precise bounding boxes/polygons, product class labeling, occlusion and quality flags, guideline-based quality checks, and exports in COCO JSON or Pascal VOC XML. 1. Tool: I prefer CVAT for detailed computer vision annotation because of its strong support for boxes, polygons, attributes, tracking, and quality control. I’m also comfortable with Label Studio and can use whichever platform fits your workflow. 2. Turnaround: I estimate 1–2 days for 100 product images, depending on image complexity and the annotation guidelines. I can confirm the estimate after reviewing the pilot batch. 3. Experience: I recently completed an Intelligent Transportation dataset in CVAT involving multiple vehicle classes, precise bounding boxes, object tracking, and correction of missed/inconsistent annotations. I’ve also used Label Studio for text annotation tasks. I can follow your positive/neutral/negative sentiment guidelines carefully for the companion text data as well. I’m happy to start with the pilot batch and will raise any ambiguous guideline early to avoid inconsistencies. Best regards, Glenn W.

@Aaishayounis

Hi, It’s Aaisha ✅Focused Requirements✅ Accurate product image annotation with bounding boxes/polygons, class labels, occlusion/quality flags, plus sentiment tagging for the accompanying text dataset. ✅My Plan✅ I prefer CVAT for precise, consistent annotation and easy dataset export. I’ll follow your guidelines carefully, clarify any ambiguity before proceeding, and deliver clean COCO JSON or Pascal VOC output with the required file structure. For 100 product images, I can typically complete the first batch within 1–2 days depending on image complexity. I can also handle positive, neutral and negative sentiment labeling using your provided template. I’m happy to start with the pilot batch so you can verify quality before scaling. Best regards, Aaisha

@venkatsreekanth

Hello, I am interested in your project and eager to learn. While I am new to image annotation and sentiment analysis, I am a fast learner with strong attention to detail and a commitment to following instructions accurately.

@anas389

Hello! I can handle your product image annotation with a strong focus on consistency, accuracy and clean dataset structure. I’ll provide: * Precise bounding boxes or polygons according to your guidelines * Correct product class labels * Occlusion and image-quality flags * COCO JSON or Pascal VOC XML export * Positive/Neutral/Negative sentiment labels for the text snippets * Consistent file and folder structure * Early clarification whenever an annotation guideline is unclear * Final quality check before delivery Preferred tool: CVAT — it provides precise annotation controls, supports both bounding boxes and polygons, and works well for structured exports such as COCO. Estimated turnaround: Around 1–2 days for 100 product images, depending on image complexity and annotation rules. I’m happy to start with your pilot batch first so you can verify the quality and consistency before assigning the full dataset. Availability: I can start immediately.

@roell3

Hello The hardest part is usually aligning data quality, evaluation, and production monitoring with real-world latency and compliance constraints. Integration with existing systems and clear ground truth often drive most of the early risk. What does the current stack look like for ingestion and deployment? Are there SLAs or compliance boundaries I should plan around? Is the work batch-only or does it require real-time inference? Looking forward to learning more about the architecture.

@rourkemurr5

I can accurately annotate your new set of product images with the detail and precision your project requires. My experience includes working on large-scale image annotation tasks that demand consistency and attention to detail. I use a systematic approach to ensure every image is labeled correctly according to your specifications, maintaining high-quality standards throughout. This helps create reliable data for your downstream uses. Do you have specific annotation guidelines or a preferred tool that I should follow?