CVAT: High-Resolution Images Not Showing On Job Pages?

Hey guys, ever been in that super frustrating spot where you've just tackled one big technical hurdle, only to be hit with another, equally puzzling one? That's exactly what we're diving into today with CVAT, especially for those of us working with high-resolution images. You see, a common issue many folks face is the dreaded "PIL.Image.DecompressionBombError" when trying to upload massive images. It’s like CVAT is telling you, "Whoa there, cowboy! That image is too big, could be a security risk!" Thankfully, smart people in the community, like those who contributed to issue 2205 on GitHub, have provided solutions. You've likely made the necessary tweaks, probably by adjusting environment variables or server configurations to increase the image processing limits, and boom, your high-resolution images are now happily uploading to CVAT. That's a huge win, seriously! But here's the kicker, and the reason we're all here: despite seeing those beautiful, crisp thumbnails of your uploaded images on the task pages, when you actually click into a job page to start annotating, poof! – no image. Just a blank canvas, or perhaps a broken icon, leaving you scratching your head and wondering, "What in the world is going on now?" It feels like you're so close, yet so far. This isn't just a minor inconvenience; for anyone doing serious computer vision work, especially with intricate details, having your high-resolution images not displaying in the browser on CVAT job pages is a showstopper. We need those pixels, every single one of them, to ensure accurate and meaningful annotations. This article is all about unraveling this mystery, troubleshooting the common culprits, and getting your high-res images to finally appear where they belong: right there on your annotation canvas. So, let's roll up our sleeves and fix this together, because nobody has time for blank canvases when there's data to label!

Unraveling the High-Res Image Mystery in CVAT: Why Your Job Pages Are Blank

So, you've conquered the beast, that pesky PIL.Image.DecompressionBombError, allowing you to upload your truly massive, high-resolution images into CVAT. That's a significant victory, truly, because working with detailed, high-pixel-count images is often critical for advanced computer vision tasks like medical imaging, satellite analysis, or intricate object detection. The fact that your images now successfully upload and even show up as clear, helpful previews on the task pages is fantastic confirmation that the initial server-side processing and storage are mostly working as intended. However, the subsequent blank canvas you encounter on the job page is a distinct and frustrating new challenge, suggesting that the problem isn't with the upload itself, but rather with how CVAT’s annotation interface renders or fetches these extremely large files for active annotation. This distinction is crucial for our troubleshooting journey. The task page preview typically uses a much smaller, downsampled thumbnail version of your image, generated during the upload process, which is quick and easy for any browser to display. It's a lightweight representation designed for overview, not detailed work. In contrast, the job page demands the full, uncompressed, high-resolution image data to be loaded and rendered, often onto a sophisticated HTML5 canvas, to allow for precise annotation tools to interact with every single pixel. This process involves a completely different set of backend services and frontend rendering capabilities, making it a much more resource-intensive operation. We need to explore various potential points of failure, from persistent server-side limitations that might be subtly affecting serving large files, to browser-specific rendering hiccups, or even peculiar behaviors within CVAT's own application logic when handling truly gigantic image assets for its core annotation workspace. This isn't just about images failing to load; it's about identifying where in this complex pipeline – from the server where the image is stored, through the network it travels, to your browser's rendering engine – the high-resolution data gets dropped or mishandled. Understanding this journey is the first big step in diagnosing and resolving why your high-resolution images are not displaying in the browser on CVAT job pages, ensuring you can finally get to the business of labeling without staring at an empty screen. The value of high-quality annotations derived from these detailed images cannot be overstated for training robust and accurate AI models, so getting this display issue sorted out is paramount for your project's success. It's a common stumbling block, and together we'll shine a light on the hidden reasons behind this blank-canvas dilemma.

Decoding the CVAT Image Pipeline: From Upload to Annotation Canvas

To effectively troubleshoot why your high-resolution images are not displaying in the browser on CVAT job pages, it's super helpful to understand the journey an image takes within CVAT, from initial upload all the way to its display on your annotation canvas. Think of it like a complex relay race, where the baton (your image data) passes through several hands, each with its own responsibilities and potential pitfalls. When you first upload an image, especially a high-resolution one, CVAT's backend services kick into gear. This involves not just saving the raw file, but often processing it: generating those useful task page previews (thumbnails), potentially converting formats, or running initial integrity checks. The fact that your previews show up perfectly confirms that this initial backend processing and storage phase, post-DecompressionBombError fix, is largely successful. The image is there, stored on the server's disk or cloud storage, and a smaller, manageable version is readily accessible for quick display. However, when you navigate to a job page for active annotation, a fundamentally different and more demanding sequence of events unfolds. The CVAT frontend, running in your browser, sends a request back to the backend for the full-resolution image data. This isn't a simple thumbnail request; it's asking for the entire, often enormous, original file. The backend then has to serve this large file. This serving process might involve different HTTP handlers, different caching mechanisms, or even different server resources compared to thumbnail generation. Once the raw image data starts streaming to your browser, the frontend's work begins. CVAT's annotation interface typically uses an HTML5 <canvas> element, often powered by JavaScript libraries or even WebGL, to render these images. This is where the magic happens – and where things can often go sideways for high-resolution images. The browser needs to: first, download the potentially massive image file completely; second, decode it into a bitmap; and third, draw that bitmap onto the canvas, all while remaining responsive to user interactions like zooming, panning, and drawing bounding boxes. Each of these steps consumes significant browser memory, CPU cycles, and potentially GPU resources. The difference in rendering between a simple <img> tag showing a thumbnail (like on the task page) and a complex canvas-based annotation tool displaying the full, raw, high-resolution image is like night and day. The task preview is a static display; the job page is an interactive, dynamic environment. Issues could arise if the server struggles to stream such a large file efficiently, if the network connection has intermittent drops for large payloads, or if the browser itself hits internal limits (like JavaScript heap size or WebGL texture limits) when trying to handle an extremely large image for interactive rendering. It’s not just about the image existing; it’s about it being served, downloaded, decoded, and rendered interactively without any hiccups. This detailed understanding of the pipeline is crucial for targeting our troubleshooting efforts directly at the specific stage where these high-resolution images are not displaying in the browser on CVAT job pages during annotation.

Common Culprits Behind Missing High-Resolution Images in CVAT

When your high-resolution images are not displaying in the browser on CVAT job pages, despite showing up perfectly on the task previews, it's time to play detective. We need to look beyond the initial DecompressionBombError fix, as that mostly handled the upload phase. Now, we're talking about the display and rendering phase, which introduces a whole new set of potential culprits. This isn't just about a simple file not found; it’s often about complex interactions between your server, network, and browser's capabilities. Let's dig into the usual suspects that can cause this specific headache.

Server-Side Configuration and Resource Limits

Even after addressing the DecompressionBombError, your server might still be a bottleneck for serving extremely large, high-resolution images to the annotation interface. The initial error was about processing images during upload, but serving them for real-time annotation is a continuous stream, and different limits might apply. Firstly, consider other server memory limits beyond the PIL-specific ones. Could your web server (like NGINX, if you're using it as a reverse proxy for CVAT) or the CVAT backend application itself be hitting a memory ceiling when trying to prepare or stream a multi-gigabyte image file? NGINX, for instance, has client_max_body_size for uploads, but also proxy_buffer_size and proxy_buffers which dictate how large data chunks it can hold and process when acting as a proxy. If these are too small, especially for colossal image files, NGINX might fail to proxy the full image data correctly to the browser, leading to incomplete downloads or timeouts without clear error messages. Similarly, look into timeout settings. If fetching a very large image takes longer than the configured server-side timeout (e.g., NGINX proxy_read_timeout or CVAT's internal API timeouts), the connection might be silently closed before the image fully downloads. This would manifest as a broken image or a perpetual loading spinner on the client side. Don't forget disk space – while the image uploaded, is there enough temporary disk space on the server if CVAT needs to perform any on-the-fly transformations or caching before serving the image? Also, double-check file permissions for the directories where CVAT stores its original, high-resolution images. If the web server process or CVAT application doesn't have read access, it simply won't be able to retrieve the image when requested by the job page. Lastly, think about CVAT specific environment variables beyond the PIL_IMAGE_MAX_PIXELS one. While not always documented for direct image serving, variables related to file handling, API timeouts, or general resource allocation for the core application might indirectly impact the delivery of massive files. Sometimes, a silent failure means the server simply gave up trying to send the full image without logging a clear error message that gets surfaced to the user. This often requires a deeper dive into the server's actual logs (e.g., Docker logs for CVAT containers, NGINX access/error logs, system logs), which we’ll cover in the troubleshooting section. Ensuring these server-side configurations are robust enough to handle the sheer size of your high-resolution images is a critical step in making them appear correctly on your CVAT job pages.

Browser-Side Rendering Challenges and WebGL/Canvas Issues

Even if the server successfully delivers the entire, high-resolution image file to your browser, the battle isn't over. Your browser itself has to render that beast, and this is where its own capabilities and limitations come into play, especially when dealing with the interactive canvas used by CVAT's annotation interface. While you might have initially dismissed the browser as the culprit, saying "it's not the problems of browser," it's worth a second look specifically for extreme resolution images. A browser might struggle to render an image that fits within general memory limits but exceeds specific GPU texture limits or JavaScript heap limits when displayed on an HTML5 canvas, especially one that's designed to be highly interactive. Modern browsers leverage WebGL for accelerated canvas rendering, but even WebGL contexts have limits on the maximum texture size they can handle. A truly colossal image (e.g., 50,000 x 50,000 pixels or more) might exceed these limits on certain hardware or browser versions, leading to a blank canvas, visual glitches, or even browser crashes. The browser console (F12, then look at the 'Console' tab) might show WebGL warnings or errors like "texture too large" or "out of memory" that aren't immediately obvious. Beyond WebGL, the raw JavaScript engine has its own heap memory limits. Decoding a gigapixel image into an in-memory bitmap for the canvas can consume a staggering amount of RAM, potentially exceeding these limits and causing the script to crash or fail silently. Furthermore, browser extensions can sometimes interfere with page rendering, especially extensions that manipulate images, scripts, or network requests. Try disabling them temporarily or testing in an incognito window. Hardware acceleration settings in your browser are also relevant; while usually beneficial, in rare cases of driver issues or specific hardware, they might cause rendering problems. You could try toggling them off in your browser's settings to see if it makes a difference. Lastly, simply using an outdated browser version can lead to compatibility issues with cutting-edge web technologies like CVAT's interactive annotation tools. Always ensure your browser (Chrome, Firefox, Edge, etc.) is fully updated to the latest stable release. The key here is that rendering an image for a dynamic, manipulable canvas is far more demanding than simply displaying a static <img> tag, and high-resolution images push these browser-side limits significantly, making them a prime suspect for why they're not displaying in the browser on CVAT job pages even when the server has done its part.

CVAT Application Logic and Frontend/Backend Discrepancies

Sometimes, the issue isn't purely server configuration or browser limitations, but rather something subtle within CVAT's own application logic when handling truly high-resolution images specifically for the annotation interface. While the task overview page successfully generates and displays a thumbnail, this might use a completely different code path than the one responsible for loading and preparing the full image for the interactive job page. Could there be a bug or feature limitation in CVAT itself that manifests only when an image exceeds a certain (internal, perhaps undocumented) size threshold for the annotation viewer component? For instance, the backend might successfully store the image, and the frontend might successfully request it, but the JavaScript logic that processes and draws the image onto the canvas might have an arbitrary internal limit or a subtle bug that causes it to fail or skip rendering for images beyond a certain pixel dimension or file size. It's possible that the database entries for your images are perfectly correct, pointing to the right file paths, but the serving mechanism for the annotation page might differ from the thumbnail generator. The thumbnail often gets created and stored as a separate, smaller file. The annotation page, however, typically tries to load the original large file. If there's any post-processing, scaling, or tiling logic that occurs only for the annotation viewer, that specific logic could be failing silently for your massive images. For example, some annotation tools implement image tiling (breaking a large image into smaller, manageable chunks) to improve performance. If this tiling mechanism itself has a bug or limit, a single, huge image might break it. Finally, consider network issues – not just general connectivity, but specific problems with transferring extremely large image data. Could the image data be too large to transfer reliably within typical HTTP request/response limits, especially under high server load or less-than-ideal network conditions? While a small thumbnail transfers quickly, a multi-gigabyte image takes time. If there are intermittent network drops, server-side proxies cutting off connections for very long transfers, or client-side network stack issues, the image transfer might never complete, resulting in a blank display. Discrepancies between how CVAT's frontend expects the image to be served and how the backend actually serves truly gigantic files can often lead to these elusive problems where high-resolution images are not displaying in the browser on CVAT job pages, demanding a closer look at the application's internal workings.

Step-by-Step Troubleshooting: Getting Your High-Res Images Back on Track

Alright, it's time to get our hands dirty and systematically troubleshoot this issue of high-resolution images not displaying in the browser on CVAT job pages. We've explored the potential culprits; now let's put on our detective hats and gather some evidence. This isn't just about trying random fixes; it's about methodically checking each stage of the image pipeline to pinpoint exactly where the breakdown is happening. Remember, the goal is not just to fix it, but to understand why it broke, so you can prevent similar issues in the future. We'll start by looking at the server, then move to the browser, and finally consider CVAT's specific configurations. This structured approach will save you a lot of headache and ensure no stone is left unturned in your quest for pixel-perfect annotation.

Inspecting Server Logs for Clues

Even after you fixed the DecompressionBombError, your server logs are still your best friend, offering a window into what the backend is actually doing (or failing to do) when you try to access those high-resolution images on a job page. This is usually the first place to look because if the server isn't delivering the image, the browser never even gets a chance to render it. If you're running CVAT with Docker, the most straightforward way to check logs is by running docker-compose logs --tail 1000 -f from your CVAT root directory. This command will show the last 1000 lines and then continuously stream new logs from all your CVAT containers. Pay close attention to logs from the cvat container (the main application) and potentially nginx if you're using it. What exactly are you looking for? Keep an eye out for any errors, warnings, or timeouts that appear specifically when you try to open a job page with a problematic high-resolution image. Look for keywords related to image, file, IOError, memory, timeout, proxy, nginx, or any 500 or 4xx status codes that might indicate server-side failures during the image serving process. For NGINX users, also check the NGINX access and error logs, typically found in /var/log/nginx/access.log and /var/log/nginx/error.log on your host machine or within the NGINX container. You're trying to see if NGINX reported any issues when trying to proxy a large image request. Are there any 413 Request Entity Too Large errors (though less likely after the upload fix, still possible if some new limit is hit)? Or perhaps 504 Gateway Timeout errors, indicating NGINX didn't get a response from the CVAT backend in time? Sometimes, the logs might not explicitly say "failed to serve image," but rather show generic memory allocation errors, process crashes, or unexpected disconnections that correlate with your attempts to load the job page. If you see recurring patterns or specific error codes, these are invaluable clues. For instance, if you're seeing Connection reset by peer or Broken pipe errors in the logs, it might suggest the server is forcefully closing the connection mid-transfer for some reason, perhaps due to internal limits being hit or the image being too large for a buffer. This meticulous inspection of server logs is absolutely essential for understanding why high-resolution images are not displaying in the browser on CVAT job pages, as it provides the backend's perspective on the problem.

Deep Dive into Browser Developer Tools

Even though you initially thought it wasn't a browser problem, we need to do a deep dive into the browser developer tools (usually by pressing F12 or right-clicking and selecting "Inspect"/"Inspect Element"). This will give us the client-side story, which complements what we found in the server logs. Go to the problematic job page, open the Dev Tools, and specifically check these tabs:

1. Network Tab: This is critical. Reload the job page with Dev Tools open. Look for all the requests made. Filter by Img or Media to isolate image requests. Do you see a request for your high-resolution image? What is its HTTP status code (e.g., 200 OK, 404 Not Found, 500 Internal Server Error, 504 Gateway Timeout)? If it's 200 OK, check the Size column. Is the full size of your high-resolution image downloaded? If it's incomplete or much smaller than expected, then the server isn't delivering it fully, or the connection is being cut. Look at the Time column – is the request taking an unusually long time, perhaps timing out? Any (failed) or (canceled) statuses are huge red flags. Pay attention to the headers as well; sometimes Content-Length might indicate the expected size, but the actual received data is less. You might also notice multiple requests for the same image, indicating retries.

2. Console Tab: Look for any JavaScript errors (red messages) or warnings (yellow messages). These can range from Uncaught ReferenceError to more specific messages related to image decoding or canvas rendering. Specifically, for high-resolution images, you might see WebGL warnings or errors like "Texture too large" or "WebGL: CONTEXT_LOST_WEBGL" if the image exceeds your GPU's texture memory limits. You might also find messages related to memory limits being hit (e.g., JavaScript heap out of memory). These errors directly tell you that the browser is struggling with the image once it has it.

3. Performance Tab: For really extreme cases, record a performance profile while loading the job page. Look for long-running JavaScript tasks, layout thrashing, or excessive memory consumption that occurs precisely when the image is supposed to render. This can show you if the browser is spending too much time trying to decode or draw the image onto the canvas, indicating a bottleneck in the rendering pipeline.

4. Memory Tab: Monitor memory usage as you load the job page. Does the JavaScript heap or overall browser memory skyrocket and then potentially crash or stabilize at an extremely high level? This can confirm that the browser is indeed running out of memory when trying to handle the enormous bitmap data of your high-resolution images. This comprehensive browser inspection is often the key to understanding client-side rendering failures when your high-resolution images are not displaying in the browser on CVAT job pages and is indispensable for effective debugging.

CVAT Configuration Tweaks and Environment Variables

After digging into logs and browser tools, it's time to revisit your CVAT configuration, particularly focusing on environment variables that might indirectly affect the serving and rendering of high-resolution images. You've already solved the DecompressionBombError by likely adjusting PIL_IMAGE_MAX_PIXELS (or a similar setting), which is great for uploads. But are there other image-related environment variables in your docker-compose.yml or CVAT settings that could be relevant? While explicit "max image size for annotation view" variables might not exist, sometimes general API timeout settings or backend processing limits can interfere. For instance, if CVAT has an internal API gateway or image processing service, it might have its own timeouts or memory limits that are independent of the main NGINX proxy settings. You might need to scour the CVAT documentation or GitHub issues for less common but impactful variables. Consider adding or increasing CVAT_API_REQUEST_TIMEOUT if such a variable exists and seems relevant, allowing more time for the backend to prepare and stream large image data. Another area to explore is scaling options for large images within CVAT. While you want to view the full-resolution image, some systems offer a way to load a downscaled proxy first, then progressively load higher resolutions as you zoom. CVAT itself might have internal logic that tries to optimize this. If that optimization logic is failing for your specific image dimensions or file size, it could result in nothing being displayed at all. It's worth ensuring that any CVAT_MAX_UPLOAD_SIZE (if applicable) is generous enough to not only accept the initial upload but also to imply sufficient backend resources for handling the image later. Sometimes, a seemingly unrelated variable, like a default image quality setting for previews, might have an unintended side effect on how the full image is handled if the system tries to apply it universally. Furthermore, if you are running CVAT in a containerized environment (which is typical), ensure that the Docker container itself has been allocated sufficient memory and CPU resources. A container with too little RAM might simply crash or hang when trying to load a massive image into memory, even if the application logic itself is sound. Review your docker-compose.yml file for mem_limit or cpus settings for the cvat service and consider temporarily increasing them significantly for testing purposes. This exploration of CVAT's internal environment variables and resource allocations is crucial for uncovering hidden limits that might be preventing your high-resolution images from displaying in the browser on CVAT job pages, moving beyond generic server or browser issues and into application-specific fine-tuning.

Testing with Smaller/Different Image Formats

Sometimes, the simplest diagnostic steps are the most effective. To isolate if the problem is purely due to the sheer size of your high-resolution images or if it's also related to their format or specific pixel dimensions, perform some targeted tests. First, try uploading a slightly smaller high-res image. For example, if your problematic images are 50,000x50,000 pixels, try one that's 20,000x20,000 pixels or even 10,000x10,000 pixels but still qualifies as "high-resolution." If these smaller, but still large, images display correctly on the job page, it strongly suggests that the issue is indeed a hard limit related to absolute file size or pixel dimensions, rather than a format-specific bug. This helps you narrow down the exact threshold at which CVAT (or your browser/server combination) starts to struggle. Second, experiment with a different image format. If your current problematic images are, say, very large PNGs, try converting a sample high-resolution image to a high-quality JPEG (with minimal compression loss). PNGs are lossless and can be significantly larger in file size than JPEGs for the same visual quality, and their decoding process might be different for the browser's rendering engine. Similarly, if you're using TIFFs, try converting to PNG or JPEG. Sometimes, a specific browser or an older version of a rendering library might have a bug or inefficiency in handling a particular high-resolution format, while performing better with another. This test helps you determine if the issue is universal to all high-resolution images or specific to a certain file type, giving you another critical piece of the puzzle. The goal here is to introduce controlled variables to your troubleshooting, allowing you to isolate the problem space. If a slightly smaller high-resolution image or a different format works, it provides clear direction on where to focus your configuration adjustments (e.g., increasing buffer sizes further, or considering client-side image optimization/tiling for extremely large dimensions) to ensure that your high-resolution images are not displaying in the browser on CVAT job pages due to format or specific size limitations.

Community Support and Advanced Debugging Strategies

When you've exhausted all the direct troubleshooting steps and your high-resolution images are still not displaying in the browser on CVAT job pages, it's time to tap into the collective wisdom of the CVAT community and, for the truly brave, consider diving into the application's source code. You're likely not the first person to hit this specific wall, and engaging with others can often shed light on obscure issues or provide workarounds you hadn't considered. This phase is about leveraging external resources and, if necessary, getting hands-on with CVAT's internals to uncover deeply embedded problems.

Leveraging the CVAT Community and GitHub

The CVAT community, particularly on GitHub, is an invaluable resource for complex issues. Before posting, always search existing issues and discussions first. There's a good chance someone else has encountered a similar problem with high-resolution images and either found a solution, a workaround, or initiated a discussion that provides further clues. Use specific keywords like "high resolution images," "large images," "job page blank," "canvas rendering," "memory limits," and reference the DecompressionBombError fix you've already applied. If you can't find an existing solution, then it's time to properly report an issue on the CVAT GitHub repository. A well-crafted issue report is half the battle. Be sure to include: a clear, concise title (like "High-Resolution Images Not Showing on Job Pages after DecompressionBombError Fix"), detailed steps to reproduce the problem (including the dimensions/file size/format of your images), information about your CVAT setup (Docker, version, OS), your browser version, and crucially, all the logs and console errors you've collected from both the server and the browser developer tools. Explicitly mention that you've already implemented the DecompressionBombError fix (referencing issue 2205 is a good idea) and that previews on task pages work, but the job pages remain blank. Screenshots, like the ones you provided, are incredibly helpful for illustrating the problem. The CVAT maintainers and other experienced users monitor these issues, and providing them with comprehensive details significantly increases the chances of a quick and accurate diagnosis. Often, a maintainer might ask you to try a specific environment variable or provide additional diagnostic information, so be prepared to respond promptly. Engaging with the community shows you've done your homework and are genuinely seeking a solution, making it more likely that you'll get the help you need to resolve why your high-resolution images are not displaying in the browser on CVAT job pages.

Diving into CVAT Source Code (For the Brave!)

For those with a strong technical background and a penchant for adventure, diving into CVAT's source code can be the ultimate troubleshooting step. This isn't for everyone, but if community support doesn't yield a solution and the problem persists, understanding the internal workings can unlock the mystery of why your high-resolution images are not displaying in the browser on CVAT job pages. You'll want to clone the CVAT repository from GitHub. Focus your efforts on two main areas: the backend image processing services and the frontend rendering logic. On the backend, look for the code that handles serving image data for the annotation interface. This might involve Django views, REST API endpoints, and internal image handling utilities. Search for file I/O operations, streaming responses, and any code that might apply transformations or checks before sending the image to the client. Pay close attention to any size checks, memory allocations, or timeout settings within the Python code itself. For the frontend, which is typically built with React and uses JavaScript, delve into the components responsible for the annotation canvas. You'll be looking for how images are loaded (Image() constructor, fetch API, etc.), how they are processed (e.g., using createImageBitmap or drawing directly to canvas.getContext('2d')), and how WebGL might be utilized. Specifically, examine any code that deals with image tiling, scaling, or memory management within the JavaScript. Look for potential try...catch blocks that might be silently catching errors related to large image processing. You could even add console logs within the JavaScript code to trace the image loading and rendering process step-by-step in your browser's developer console. Setting up a local development environment for CVAT would allow you to make small code changes and test them directly, using a debugger to step through the execution flow. This deep dive can reveal subtle bugs, unexpected limits, or edge cases that only manifest with truly high-resolution images, giving you the power to either fix it yourself or contribute a highly specific bug report that leads to an official patch. While challenging, exploring the source code is often the most definitive way to understand and resolve complex application-specific issues like this.

Final Thoughts: Don't Let High-Res Images Hold You Back!

Alright team, we've covered a lot of ground in our quest to get your high-resolution images properly displaying in CVAT's annotation job pages. It's a journey that often starts with one challenge (like the DecompressionBombError) and then throws another curveball at you. But remember, the goal here is to empower you to tackle these issues head-on. Don't let blank canvases frustrate your progress, especially when you're dealing with vital, high-quality data. The effort you put into ensuring your high-resolution images are not displaying in the browser on CVAT job pages is directly proportional to the quality and robustness of the AI models you'll ultimately train. Accurate annotations, especially for intricate details, demand those precious pixels to be visible and manipulable. By systematically checking your server logs, thoroughly dissecting your browser's developer tools, fine-tuning CVAT's configuration and environment variables, and smartly testing with different image properties, you're building a comprehensive understanding of the entire image pipeline. And if all else fails, the power of the CVAT community and the transparency of its open-source code are always there to back you up. So, keep pushing forward, keep debugging, and you'll undoubtedly get those magnificent, high-resolution images to shine brightly on your CVAT annotation canvas, where they belong! Happy annotating, guys!