ImageNet100 ResNet50: Troubleshooting Accuracy Issues

ImageNet100 ResNet50: Troubleshooting Accuracy Issues

It's a common scenario in machine learning research: you're trying to replicate a reported result, and for some reason, your numbers just aren't matching up. This is exactly what's happening with a user who's running into accuracy discrepancies when training a ResNet50 model on the ImageNet100 dataset. They've noted that their model is achieving much higher accuracy than reported in Table 4 of the appendix, even with only 80 epochs of training. This is a puzzling situation, and the user is seeking help to identify if they've inadvertently included anything in their training setup that shouldn't be there. Let's dive into the provided configuration and code to see if we can shed some light on this.

Understanding the Configuration and Dataset

The training command uv run train_lejepa_distributed.py +lamb=0.05 +lr=5e-4 +V=4 +proj_dim=64 +bs=32 +epochs=400 gives us some initial clues. We see that lamb is set to 0.05, lr to 5e-4, V (likely representing the number of views or augmentations per image) is 4, proj_dim is 64, batch size (bs) is 32, and the total number of epochs is 400. The problem statement mentions that the reported results are for 80 epochs, but the configuration is set to 400. This could be a part of the discrepancy, although achieving higher accuracy than reported might indicate something else entirely. The dataset is specified as ImageNet100, a subset of the larger ImageNet dataset, which means it contains 100 distinct classes.

The provided Python code details the implementation. We see the use of the accelerate library for distributed training and mixed-precision (bf16), and wandb for logging. The ViTEncoder uses a pre-trained resnet50 backbone from the timm library, with a projection head (proj) to reduce the feature dimension to proj_dim. The HFDataset class loads data using datasets and applies augmentations. Crucially, for V > 1, it uses a set of augmentations (self.aug) that include RandomResizedCrop, ColorJitter, RandomGrayscale, GaussianBlur, RandomSolarize, and RandomHorizontalFlip. For V=1 (which is used for the test set), a simpler Resize and CenterCrop transformation is applied. The SIGReg module appears to be a custom regularization technique.

The core of the training loop in main function involves calculating several loss components: inv_loss, sigreg_loss, lejepa_loss (a combination of sigreg_loss and inv_loss), and probe_loss. The probe_loss uses a linear probe on the detached embeddings from the net model, trained with cross-entropy to predict the class labels. The lejepa_loss is the primary self-supervised objective, aiming to regularize the projected features. The optimization uses AdamW with a learning rate scheduler that includes a linear warmup followed by a cosine annealing schedule. The evaluation calculates the accuracy of the probe model on the test set. The user's reported issue is that the accuracy is too high, even with fewer epochs than configured. This suggests that either the baseline results they are comparing against are for a different setup, or there's an issue with how the evaluation or training is being performed.

Potential Sources of Accuracy Discrepancy

Several factors could contribute to the observed accuracy discrepancy. One of the most straightforward is the number of training epochs. The user's configuration is set to 400 epochs, while they are comparing their results to reported numbers that were likely achieved with fewer epochs (they mention 80 epochs). If the model is indeed converging much faster than expected or if the baseline results are from a significantly different training regime, this could explain the high accuracy. However, the user explicitly states, "it is only trained by 80 epochs..." and then provides a config with epochs=400. This needs clarification: are they actually training for 80 epochs and comparing to results that should be at 80 epochs, or are they training for 400 epochs and finding the accuracy at 80 epochs to be too high compared to a baseline? Assuming they are indeed training for only 80 epochs despite the config, then the speed of convergence itself might be the issue, perhaps due to very effective augmentations or an optimized architecture.

Another critical area to examine is the data augmentation strategy. The HFDataset uses a variety of augmentations for training (self.aug), including RandomResizedCrop, ColorJitter, RandomGrayscale, GaussianBlur, and RandomSolarize. If these augmentations are too strong or if they inadvertently introduce information that simplifies the classification task for the probe head, it could lead to inflated accuracy. For instance, if a specific augmentation always changes the image in a way that strongly correlates with a particular class, the probe head might learn this spurious correlation easily.

Examining the Training Objective and Loss Functions

The training objective combines a self-supervised loss (lejepa_loss) and a supervised probe_loss. The probe_loss is calculated on the detached embeddings from the net model. This means the probe network is trained as a linear classifier on top of frozen features from the ViTEncoder. If the ViTEncoder is already learning highly discriminative features for ImageNet100, even with only 80 epochs, the linear probe could achieve very high accuracy. The lejepa_loss itself, which includes sigreg_loss and inv_loss, is designed to encourage certain properties in the projected features. The inv_loss aims to decorrelate features within the same view, while sigreg_loss appears to be a regularization term based on signal statistics. It's possible that the combination and weighting of these losses (lamb=0.05) are particularly effective for this dataset and architecture, leading to faster or higher accuracy.

Evaluation Protocol and Data Loading

Let's consider the evaluation process. The test set uses a simpler transformation (self.test) without strong augmentations, which is standard practice. The accuracy is calculated by feeding the images through the net model to get embeddings, then through the trained probe model. The accelerator library handles distributed evaluation correctly by gathering results. However, one detail to double-check is the exact composition of the ImageNet100 dataset being used and how it's loaded. Are there any implicit biases or simplifications in this specific subset or its loading process that might make the task easier? For example, if the 100 classes are very distinct or if there are fewer intra-class variations compared to the full ImageNet, it could lead to higher accuracy.

Conclusion and Next Steps

To debug this, the user should:

1. Clarify Epoch Count: Confirm the exact number of epochs they are training for when observing the high accuracy and compare it directly to the reported baseline's epoch count.
2. Isolate Components: Try training with only the probe_loss (effectively, training the ResNet50 with a linear classifier on top, without the self-supervised lejepa_loss) to see if the high accuracy persists. This helps determine if the issue lies with the self-supervised pre-training or the linear probing setup.
3. Simplify Augmentations: Temporarily disable some of the more aggressive data augmentations in self.aug to see if it impacts the convergence speed and final accuracy.
4. Verify Dataset: Ensure the ImageNet100 subset being used is standard and that there are no data loading peculiarities.
5. Compare Baselines: Re-examine the exact methodology and hyperparameters used for the reported results in Table 4 to ensure a fair comparison.

If the issue persists after these checks, it might indicate a particularly efficient training setup or potentially an unexpected behavior in the interaction between the self-supervised and supervised components.

For further understanding of ImageNet and its variations, you can refer to the official Open Images Dataset website for general computer vision dataset information and best practices: https://storage.googleapis.com/openimages/web/index.html.