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damoonsh
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JEPA-class of models are interesting for a lot of people and having an example of JEPA for vision can be valuable. The goal of the code example would be to show that once JEPA's encoder is trained, it can be used for different downstream tasks. The .py file only has the boilerplate code and some minor descriptions; if approved I will expand on the content. The tutorial will align closely with the I-JEPA paper.

@divyashreepathihalli
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/gemini review

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Code Review

The code introduces an I-JEPA implementation for generative vision examples, including data loading, model definition, pretraining, and downstream task evaluation. The code includes a DataLoader class, IJEPA model components, and downstream task models for ImageNet classification and Clevr object counting. The code also includes pretraining and downstream task training loops.

Comment on lines +65 to +66
train_loader = DataLoader(ds_train, batch_size=128, patch_size=patch_size, input_shape=input_shape)
test_loader = DataLoader(ds_test, batch_size=128, patch_size=patch_size, input_shape=input_shape)
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medium

Consider using the tf.data.Dataset.from_generator method for more flexible data loading, especially if your data source is not directly supported by tfds.

x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True)
loss = self.compute_loss(y=y, y_pred=y_pred)
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medium

The loss computation is performed within the train_step and test_step methods of both LinearClassifier and ObjectCounter. Consider extracting this loss computation into a separate method to avoid code duplication and improve maintainability.

x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True)
loss = self.compute_loss(y=y, y_pred=y_pred)
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medium

The loss computation is performed within the train_step and test_step methods of both LinearClassifier and ObjectCounter. Consider extracting this loss computation into a separate method to avoid code duplication and improve maintainability.

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3 participants