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README.md

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-license: mit
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+# Dataset Card for Tiny ImageNet
+
+<!-- Provide a quick summary of the dataset. -->
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+In Tiny ImageNet, there are 100,000 images divided up into 200 classes. Every image in the dataset is downsized to a 64×64 colored image. For every class, there are 500 training images, 50 validating images, and 50 test images.
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Homepage:** https://www.kaggle.com/c/tiny-imagenet
+- **Paper:** Le, Y., & Yang, X. (2015). Tiny imagenet visual recognition challenge. CS 231N, 7(7), 3.
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+Total images: 100,000
+
+Classes: 200 categories
+
+Splits:
+
+- **Train:** 100,000 images
+
+- **Validation:** 10,000 images
+
+- **Test:** 10,000 images
+
+Image specs: JPEG format, 64×64 pixels, RGB
+
+## Example Usage
+Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
+```
+from datasets import load_dataset  
+
+# Load the dataset  
+dataset = load_dataset("randall-lab/tiny-imagenet", split="train", trust_remote_code=True)
+# dataset = load_dataset("randall-lab/tiny-imagenet", split="validation", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/tiny-imagenet", split="test", trust_remote_code=True)  
+
+# Access a sample from the dataset  
+example = dataset[0]  
+image = example["image"]  
+label = example["label"]  
+
+image.show()  # Display the image  
+print(f"Label: {label}")
+```
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+@article{le2015tiny,
+  title={Tiny imagenet visual recognition challenge},
+  author={Le, Yann and Yang, Xuan},
+  journal={CS 231N},
+  volume={7},
+  number={7},
+  pages={3},
+  year={2015}
+}