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mdouze
2017-02-28 10:24:01 +01:00
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@@ -39,7 +39,7 @@ To run it, please download the ANN_SIFT1M dataset from
http://corpus-texmex.irisa.fr/
and unzip it to the sudirectory sift1M.
and unzip it to the subdirectory sift1M.
### Result
@@ -89,11 +89,11 @@ database files to base.fvecs and the training files to learn.fvecs
### Running the experiments
These experiments are quite long. To support resuming, the script
stores the result of raining to a temporary directory, `/tmp/bench_polysemous`.
stores the result of training to a temporary directory, `/tmp/bench_polysemous`.
The script `bench_polysemous_1bn.py` takes at leas two arguments:
The script `bench_polysemous_1bn.py` takes at least two arguments:
- the dataset name: SIFT1000M (aka SIFT1B, aka BIGANN) or Deep1B. SIFT1M, SIFT2M,... are also supported to make subsets of for small experiments (note that SIFT1M is not the same as the SIFT1M above)
- the dataset name: SIFT1000M (aka SIFT1B, aka BIGANN) or Deep1B. SIFT1M, SIFT2M,... are also supported to make subsets of for small experiments (note that SIFT1M as a subset of SIFT1B is not the same as the SIFT1M above)
- the type of index to build, which should be a valid [index_factory key](https://github.com/facebookresearch/faiss/wiki/High-level-interface-and-auto-tuning#index-factory) (see below for examples)
@@ -158,6 +158,8 @@ nprobe=32,ht=28 0.1256 0.3563 0.5026 0.561 52.61
```
Here again the runs are not exactly the same but the original result was obtained from nprobe=32,ht=28.
For Deep1B, we used a simple version of [auto-tuning](https://github.com/facebookresearch/faiss/wiki/High-level-interface-and-auto-tuning/_edit#auto-tuning-the-runtime-parameters) to sweep through the set of operating points:
```
python bench_polysemous_1bn.py Deep1B OPQ20_80,IMI2x14,PQ20 autotune
...