Comments (1)
PVGorshenin
commented on May 18, 2024
1
@Vuong02011996 I assume you are using model of class Model, defined in retinaface.predict_single. predict_json is the inference just in case of one image.
Take a look on retinaface.inference. It is inference for the batch form. It has data preparation and loading under the hood. You have to pass arguments to script in the command line. It seems no explanation in the READ.me. But after taking a look on the code in inference.py things start to be clean.
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Related Issues (19)
- RuntimeError during get_model HOT 1
- Validation accuracy & Mobilenet HOT 9
- Inference using GPU HOT 1
- Default parameter values HOT 1
- RGB vs. BGR HOT 2
- google-colab 1.0.0 has requirement ipykernel~=4.10 HOT 1
- Unintuitive output of predict_jsons()
- map
- Spellings mistake in repository README and PyPi description page
- Negative values in the predicted annotations HOT 2
- Some questions about training on my custom datasets? HOT 6
- Fixed bug in the training code! HOT 4
- Bug: Inference on cuda results in fixed bounding box offset! HOT 2
- Does this project use focal loss?
- Suggest to loosen the dependency on albumentations
- How to train with custom dataset by using the pretrained model?
- Why def _pad_to_square do not to adjust the landmark? HOT 3
- How Syncronized BatchNorm implemented HOT 1
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