loss is not decreasing, and stay about 10
training is based on VOC2021 images (originally 20 clasees and about 15000 images), i added there 1 new class with 40 new images.
i use:
ssd_inception_v2_coco model.
Python 3.6.13
tensorflow 1.15.5
I have to use tensorflow 1.15 in order to be able to use DirectML because i have AMD GPU
Here are potential causes and solutions for the stagnant loss in your object detection training scenario:
1. Insufficient Data for New Class:
40 images might be inadequate for a new class, especially with a complex model like ssd_inception_v2_coco.
Solutions:
Gather more images for the new class (aim for a few hundred or more).
Utilize data augmentation techniques (random cropping, flipping, color jittering) to artificially increase diversity.
2. Imbalanced Dataset:
The original 20 classes have significantly more data than the new class, potentially biasing the model towards them.
Solutions:
Oversample the new class during training to give it more weight.
Use class weighting techniques to balance the importance of different classes during loss calculation.
3. Learning Rate Issues:
An inappropriate learning rate might hinder model convergence.
Solutions:
Experiment with different learning rates (e.g., smaller values like 1e-4 or 1e-5).
Implement learning rate scheduling to gradually decrease the learning rate during training.
4. Overfitting:
The model might be overfitting to the training data, preventing generalization to new examples.
Solutions:
Employ early stopping to halt training when validation loss starts increasing.
Use regularization techniques like L1/L2 weight decay or dropout to reduce overfitting.
5. Incorrect Configuration:
Double-check model configuration, data loading, and training setup for errors.
Solutions:
Verify label mapping, data preprocessing, and loss function configuration.
Ensure proper model architecture loading and training loop implementation.
Additional Troubleshooting Steps:
Visualize Training: Plot loss and accuracy curves for both training and validation sets to identify potential issues like overfitting or underfitting.
Monitor Gradients: Check for vanishing or exploding gradients, which can impede learning.
Experiment with Hyperparameters: Adjust batch size, optimizer, and other hyperparameters to find the optimal settings for your dataset.
Explore Transfer Learning: Consider using a pre-trained model on a larger dataset like COCO and fine-tuning it for your specific task. This can often lead to better performance with limited data.