Training Loss Increasing After Initial Decrease with Custom Video Dataset

[DaBihy]

Hello Everyone,

I've been working with the V-JEPA model for a self-supervised learning project using a custom video dataset. Initially, the training loss decreases as expected, but starts to increase significantly after reaching a minimum. This behavior persists across multiple training sessions with different hyperparameters.

Configuration:

Data Setup

• Dataset Type: VideoDataset
• Batch Size: 24
• Number of Clips: 1
• Number of Frames per Clip: 16
• Tubelet Size: 4
• Sampling Rate: 2
• Crop Size: 224
• Patch Size: 16
• Memory Pinning: true
• Number of Workers: 8
• Filter Short Videos: false

Data Augmentation

• Auto Augment: false
• Motion Shift: false
• Random Resize Aspect Ratio: [0.75, 1.35]
• Random Resize Scale: [0.3, 1.0]
• Re-probability: 0.0

Loss Configuration

• Loss Exponent: 1.0
• Regularization Coefficient: 0.0

Mask Settings

• Aspect Ratio: [0.75, 1.5]
• Number of Blocks: [8, 2]
• Spatial Scale: [0.15, 0.7]
• Temporal Scale: [1.0, 1.0]
• Max Temporal Keep: 1.0

Meta Configuration

• Seed: 234
• Evaluation Frequency: Every 100 epochs
• Use SDPA: true
• Data Type: float16

Model Configuration

• Model Name: vit_small
• Predictor Depth: 12
• Predictor Embedding Dimension: 384
• Uniform Power: true
• Use Mask Tokens: true
• Zero Initialize Mask Tokens: true

Optimization

• Iterations per Epoch: 300
• IPE Scale: 1.25
• Gradient Clipping: 10.0
• Weight Decay: 0.04
• Final Weight Decay: 0.4
• Epochs: 500
• Warmup: 40
• Start Learning Rate: 0.0004
• Learning Rate: 0.000825
• Final Learning Rate: 1.0e-06
• Exponential Moving Average: [0.998, 1.0]

Questions:

1. Has anyone else encountered similar issues when training on custom datasets, particularly with video data?
2. Are there recommended strategies for adjusting the training regimen or model configuration that might stabilize the loss?
3. Could this be related to the specific characteristics of video data in the custom dataset that might require different handling or preprocessing?

Any insights or suggestions would be greatly appreciated. Thank you for your support!

Best regards,

@MidoAssran

[icekang]

Hi,

I have the same problem. Although, I resume from the lastest vjepa at epoch 300 (Plot of jepa-loss)

However, looking at regression regularization loss, it seems to be continually optimized over-time.

[DaBihy]

@icekang thank you for you comment, I can confirm that I have the same thing for reg loss:

The model is learning even though the JEPA loss is increasing. It's counterintuitive, but I think it's normal behavior for such frameworks as I observe the same thing when training BYOL.

[icekang]

Sorry, it was not regression loss, it is regularization loss regarding the variance of the predicted vector Anyway, I think I should all be decreasing, especially jepa loss which indicates that the predicted feature vector is close to the actual feature vector