Comments (4)
Effectively you can remove all the download file logic etc, and then you put the prefix of your jpg images for example like this:
image_paths = '<HERE GOES YOUR PATH>'
list_files = glob.glob(os.path.join(images_path, '**.jpg'))
The only constraint it has is to have all your images in a single folder.
from visualsearch_mxnet.
this approach uses KNN search on the deep features of the test image for inference.
Look at the code here: https://github.com/ThomasDelteil/VisualSearch_MXNet/blob/master/mms/visualservice.py#L42
from visualsearch_mxnet.
Thanks for that.
how about Creating data files for offline inference?
do i need to work with this same?
from visualsearch_mxnet.
Cool Thanks. Will check this
from visualsearch_mxnet.
Related Issues (15)
- Can we have the results with hyperlinks to the page? HOT 2
- Fine tuned model HOT 3
- Issue creating model archive HOT 8
- IndexError: index 0 is out of bounds for axis 0 with size 0 HOT 1
- Custom Codes in visualsearch.py
- Same result when multiple calls at the same time
- Can I have a function like SIFT / SURF ? HOT 1
- HNSW model HOT 4
- Did you visualise the data with t-SNE or something? HOT 1
- How to train model in our own data HOT 1
- Unable to locate visualsearch.model file HOT 3
- Inconsistencies in Predictions HOT 1
- Failure when Running Training Model on AWS Batch HOT 5
- Service input file HOT 2
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