OV-COCO Evaluation: Addressing Performance Inconsistencies

Unpacking Performance Discrepancies in OV-COCO Evaluation with F-ViT + FineCLIP

Hey there! It's awesome to see you diving into the world of F-ViT and FineCLIP. I totally get the excitement, and I'm stoked you're trying out the codebase. It's fantastic that you're digging into the nuances of model performance. When you're dealing with cutting-edge models like F-ViT and FineCLIP, understanding the intricacies of evaluation, especially on datasets like OV-COCO, is super important. It's like being a detective, piecing together clues to figure out why your results might be a little different from what you expect. So, let's break down your experience and see if we can uncover some insights together.

First off, I hear you loud and clear on the performance discrepancy. It's completely valid to notice that your OV-COCO results – specifically the base_ap50, novel_ap50, and all_ap50 scores – are lower than those reported in the original paper. Getting results that don't quite match up can be a bit of a head-scratcher, right? Especially when you're following the training code and using the same model architecture. Don't worry, this is actually a pretty common experience in the world of machine learning, and it's often due to subtle differences in the experimental setup or evaluation protocol. Let's delve into what might be going on, and explore ways to address the potential disparities you've observed.

Now, let's explore some of the potential culprits behind these variations. One of the primary suspects is the data splits used for evaluation. This is a very key area to investigate, as different splits can significantly impact the final results. When evaluating models on OV-COCO, the way you divide your dataset into training, validation, and testing sets is super critical. Even small changes in the composition of these sets can cause variations in the performance metrics. The choice of which images are included in the 'novel' category, and how those categories are defined, can lead to substantial differences in the novel_ap50 score.

Next, the checkpoint itself. What's the exact state of the model when you're evaluating it? The checkpoint refers to the specific weights and biases of the model at a given point during training. If you're using a different checkpoint than the one used in the original paper, the results could vary. This is because the model's performance evolves as it trains. Maybe the original paper reported the results at the very end of training, or the paper used a different checkpoint selection method (like picking the best-performing checkpoint on a validation set). Remember, training is a dynamic process, and model weights change over time.

Finally, we have specific settings. Subtle differences in the evaluation protocol can also contribute to variations in performance metrics. These can include things like the image preprocessing steps (resizing, normalization), the way bounding boxes are calculated, or even the specific tools and libraries used for evaluation. Any of these could lead to variations, so it's critical to make sure that these settings are identical to the settings used in the paper. Make sure to carefully examine the details of the evaluation procedure to see if any of these settings might be a source of the difference.

In essence, you are doing a good job in identifying that any of these three areas might be the reason for your results.

Detailed Insights on OV-COCO Evaluation

When evaluating a model like F-ViT with FineCLIP on OV-COCO, you're not just looking at how well the model can identify objects in images. You're also testing its ability to generalize to objects it hasn't seen before – hence the