Tensor Classification 🌍

The task of TensorClassification involves the identification and categorization of specific features within the entire tensor system.

📌 Inputs:

• SampleTensor [input]:
  • A tensor ranging from 1D to 5D.

• SampleTensor [target]: (optional)
  • A 1D tensor with integers representing discrete values.

• SampleTensor [extra]: (optional)
  • Additional tensors that may assist in exploratory data analysis or the training process.

🛠️ Use Cases:

• Urban Planning: Helps in monitoring urban expansion over time and predicting future developments.
• Environmental Studies: Assists in detecting changes in forest cover or water bodies.
• Agriculture: Evaluates the health and types of crops, aiding in precision agriculture.

🔍 Example:

import mlstac

name = "https://huggingface.co/datasets/mlstac/tensor_classification_demo.json"
dataset = mlstac.dataset(name, streaming=True, framework="torch")
print(next(iter(dataset)))

# Output example:
# {'input': tensor([[[[...]]]]), 'target': tensor([2, 5, 15])}