Comments (7)
Hi,
Files mentioned there cannot be redistributed due to licensing. Some examples also uses training data sets that cannot be distributed with Dali.
We plan to create license free set of images so others could use it during own development.
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So could you please make that happen as soon as possible as self-testing is CRITICAL? @JanuszL
We plan to create license free set of images so others could use it during own development.
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Hi,
We are working on that. Images itself are not a problem, we have them ready. The problem is that we have found other issues with this new images and we want to fix them first. So when we provide users the ability to do self-test we want to be sure that all tests pass.
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Tracked internally in DALI-132
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PR is created #69
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We have created a repo with the test data - https://github.com/NVIDIA/DALI_extra. We plan to gradually move all tests to it.
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#1056 should fulfill this request.
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Related Issues (20)
- random_resized_crop does not support different image sizes HOT 13
- How exactly does distributing the dataset with DALI/TensorFlow work? HOT 3
- Video Reader performance and alternative considerations HOT 4
- `experimental_fetch_to_device` makes no difference since TF 2.14 HOT 2
- external_source doesn't return last incomplete batch HOT 5
- Video Reader sequence start and end HOT 3
- Is it possible to modify the labels according to the random rotation of images? HOT 5
- Create a pytorch dataloader with 2 data sources HOT 5
- Resize on CPU with output shape coming from an external source operator HOT 3
- webdataset error HOT 2
- AWS DALI video_loader error HOT 3
- why multi-gpu training slower than single gpu HOT 1
- How to manage empty items in a batch? HOT 5
- nvidia.dali.fn.python_function() failed with TypeError HOT 5
- Can I install DALI on CPU node without GPU/CUDA HOT 3
- fn.lookup_table giving wrong result for gpu HOT 5
- Need argmax/argmin operator HOT 1
- How to destory a pipeline? HOT 1
- random crop function HOT 10
- cv2.resize(interpolation=cv2.INTER_LINEAR) different with fn.resize(img, antialias=True, subpixel_scale=True, interp_type=types.DALIInterpType.INTERP_LINEAR, resize_y=target_img_height, max_size=max_width) HOT 2
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