Comments (8)
@trungpham2606 I got the above results in 20K iterations, which is part of the default configuration. No, I haven't tried with unknown data.
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@sampathchanda Hello, Iam also trying this repository. Can you tell me how many epochs you needed to achieve that results :-? Btw, have you tried to test this model with unknown data to verify whether or not this model can distinguish 2 images (at the same class or not) ?
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@sampathchanda Can you please send me your pre-trained weights ?, Iam working in distinguish images on my own dataset.
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@sampathchanda Very sorry for the late reply. We did achieve better results than those in the paper since we only reported average numbers. So your results are not surprising :) Btw, the best result we got is about 0.81 of recall@1. Also, we changed the initialization method from tf.truncated_normal_initializer(stddev=0.1) to tf.glorot_uniform_initializer() since the latter leads to more stable performance.
For CUB dataset, there should be no problem. I will include hyperparameters for CUB soon, hopefully before sunset.
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@wzzheng When can you public your pre-trained weights :3 Iam so excited looking forward to it
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@trungpham2606 Hi, if you could tell me your email address, I will send a pretrained model to you.
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@sampathchanda Hi, I've added the recommended hyperparameters for CUB-200-2011, I hope they will solve your problem.
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@wzzheng here's my gmail: [email protected] Thank you so much! Iam also using your model to solve images classification problem by image similarity. But the results I got so far are not good enough, maybe due to the lack of training iterations.
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Related Issues (16)
- code problem HOT 2
- Inferences HOT 2
- running on gpu
- [Maths] Symbol in the paper? HOT 7
- code problem HOT 1
- How to run the code for the triplet loss? HOT 2
- What about the way of loading data in tfrecord? HOT 1
- Hi What does this step mean? HOT 1
- code is a bit problematic HOT 3
- Error loading data? HOT 1
- Error for products dataset
- some questions about train and evaluate my own dataset HOT 4
- Configuration on SOP dataset HOT 2
- the question about evaluate.py HOT 1
- Embedding Normalization for Triplet Loss
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