Comments (5)
leonoverweel
commented on July 24, 2024
1
Hi, thanks for checking out Larq Zoo. @timdebruin and I went back through some old experiments to find their top-1 accuracies for you; here's the details:
| Stage | Description | Epochs | Train acc | Val acc |
|---|---|---|---|---|
| 1 - ResNet | FP weights, FP activations | 100 | 70.87% | 70.32% |
| 2 - R2B_FP | FP weights, FP activations | 75 | 69.53% | 71.21% |
| 3 - R2B_BAN | FP weights, binary activations | 75 | 63.70% | 67.12% |
| 4 - R2B_BNN | Binary weights, binary activations | 100 | 62.57% | 65.04% |
Hope this helps!
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leonoverweel
commented on July 24, 2024
1
No problem. Sadly I couldn't find our experiment data for the strong baseline, but from my notes at the time I think we got 62% validation accuracy.
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Hyungjun-K1m
commented on July 24, 2024
Great! I really appreciate your fast response! Do you also have the results for strong baseline?
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Hyungjun-K1m
commented on July 24, 2024
Cool! 62% is even higher than what is reported on the paper! Do you have any thoughts on what caused this improvement?
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appleleaves
commented on July 24, 2024
I can only reach about 65% training
ResNet | FP weights, FP activations | 100
I used 8 cards and simply ran the code.
Would you like to share some models or advice?
@leonoverweel
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Related Issues (20)
- Unexpected behavior of the "include_top" argument HOT 3
- Unexpected behavior of the "preprocess_input" function HOT 1
- Make ordering of docstring constistent
- Snapshot tests of model summaries HOT 2
- RFC: structure change HOT 3
- Add model accuracies to docstrings
- QuickNet(Large) models don't match released h5 files
- Support TensorFlow 2.2 HOT 3
- QuickNet model and flip_ratio metric do not work together HOT 3
- Update weights and parameters in docstrings
- QuickNet no-top models pretrained weights are not working as expected HOT 1
- No 'sota' module HOT 2
- Speech Models HOT 2
- About RealToBinaryNet model HOT 14
- Data directory HOT 1
- Reproducing R2B model HOT 6
- Help, no logs are printed! HOT 2
- The usage of data.cache() causes the run out of memory. HOT 5
- Drop Python 3.6 support
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