Comments (2)
omoindrot
commented on June 6, 2024
What are you measuring when you say "performance is not good"?
If you are looking at embeddings on a separate test set, it is possible that the model overfits with 6 identities. Maybe you could add regularization or increase the number of identities / images?
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FSet89
commented on June 6, 2024
It seems that the embeddings are very similar, no matter what image is fed. I will try to increase the dataset.
from tensorflow-triplet-loss.
Related Issues (20)
- What does 'embedding_mean_norm' mean? HOT 2
- loss=0 in step=101(after two step) HOT 1
- Saving weights of model and calculation of embeddings HOT 1
- Using batch_all_triplet_loss function HOT 2
- Embeddings Collapse very fast
- fraction_positive increasing HOT 4
- the function of _get_triplet_mask in triplet_loss.py
- base_model
- Multi domain triplet loss
- Why average loss value by `batch_size` when using `batch_hard` method?
- PyTorch Implmentation of triplet loss
- the difference between tf.contrib.losses.metric_learning.triplet_semihard_loss and batch_all_triplet_loss
- Implementation of metrics to monitor training process in tf.keras environment
- Advice on which loss to optimize HOT 2
- Performance issues in the program
- Performance issue in /model/tests (by P3) HOT 1
- Got Nan value when label's id is greater than 159 HOT 1
- hard triplet convergence!
- Some corrections in MNIST_dataset since TF 2.0
- OSS License compatibility question
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