Create README.md

README.md

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+---
+license: mit
+task_categories:
+- text-classification
+---
+# latent_imagenet (VQGAN-encoded ImageNet Test Set)
+
+## Dataset Summary
+
+This dataset contains latent representations of the **ImageNet test set**, generated using a **VQGAN encoder**.  
+Each image is encoded into a tensor of shape **3 × 64 × 64** in the latent space.
+
+This latent dataset can be used for:
+- Accelerating training and inference in generative models (e.g., diffusion models in latent space)
+- Research on semantic compression and generative modeling
+- Experiments with reconstruction, super-resolution, and latent editing
+
+---
+
+## Dataset Details
+
+- **Source dataset**: [ImageNet](https://image-net.org/)
+- **Subset**: `test` split
+- **Preprocessing**: Images were encoded into latent tensors using a pre-trained VQGAN encoder.
+- **Latent shape**: `3 × 64 × 64`
+- **File format**: PyTorch tensor (`.pt` / `.pth`) or NumPy array (depending on your processing pipeline)
+- **Number of samples**: Equal to the ImageNet test set size.
+
+---
+
+## Example Usage
+
+```python
+from datasets import load_dataset
+import torch
+
+# Load the dataset
+dataset = load_dataset("liangzhidanta/latent_imagenet")
+
+# Access one sample
+sample = dataset["train"][0]  # or "test" depending on your split
+latent = torch.tensor(sample["latent"])  # shape: [3, 64, 64]