YOLOV8 port to keras-hub #1899
YOLOV8 port to keras-hub #1899
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| label_encoder=None, | ||
| prediction_decoder=None, | ||
| **kwargs, | ||
| ): |
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restructure the code to define all teh layers first, functional model next and config last with these comments
=== Layers ===
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=== Functional model ===
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=== Config ===
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example : https://github.com/keras-team/keras-hub/blob/master/keras_hub/src/models/bert/bert_backbone.py#L92
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Hi Divya, the current model applies multiple blocks of layers. That will imply that we would need to initialize many layers in the constructor. Moreover, the connections between those layers are not so straightforward as in Bert. What would you suggest? Shall we still initialize all layers in the layer block and connect them in the functional block?
| def predict_step(self, *args): | ||
| outputs = super().predict_step(*args) | ||
| if isinstance(outputs, tuple): | ||
| return self.decode_predictions(outputs[0], args[-1]), outputs[1] |
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will model.fit work ?
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| import os | |||
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add generic task test - self.run_task_test - example : https://github.com/keras-team/keras-hub/blob/master/keras_hub/src/models/bert/bert_text_classifier_test.py#L41
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you will need to add preprocessor flow - example follow resnet - https://github.com/keras-team/keras-hub/tree/master/keras_hub/src/models/resnet
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| @pytest.mark.large # Saving is slow, so mark these large. | ||
| def test_saved_model(self): | ||
| model = keras_hub.models.YOLOV8Detector( |
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use self.run_model_saving_test - example : https://github.com/keras-team/keras-hub/blob/master/keras_hub/src/models/resnet/resnet_image_classifier_test.py#L62C9-L62C35
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| # TODO(tirthasheshpatel): Support updating prediction decoder in Keras Core. | ||
| @pytest.mark.skip(reason="Missing presets") | ||
| @pytest.mark.tf_keras_only |
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no tf_keras_only in KerasHub
…only implementation
This PR ports YOLOV8 from keras-cv to keras-hub (#176). All necessary YOLOV8 functions are now found inside keras-hub:
Missing steps include: