Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- vision-transformer
+- ViT
+- classification
+- cifar10
+- computer-vision
+- deep-learning
+- machine-learning
+---
+
+# ViT-Classification-CIFAR10
+
+## Model Description
+
+This model is a Vision Transformer (ViT) architecture trained on the CIFAR-10 dataset for image classification. It is trained from scratch without pre-training on a larger dataset.
+
+**Metrics:**
+
+* Test accuracy: 82.04%
+* Test loss: 0.5560
+
+## Training Configuration
+
+**Hardware:** NVIDIA RTX 3090
+
+**Training parameters:**
+
+* Epochs: 200
+* Batch size: 2048
+* Input size: 3x32x32
+* Patch size: 4
+* Sequence length: 8*8
+* Embed size: 128
+* Num of layers: 12
+* Num of heads: 4
+* Forward multiplier: 2
+* Dropout: 0.1
+* Optimizer: AdamW
+
+## Intended Uses & Limitations
+
+This model is intended for practice purposes and exploration of ViT architectures on the CIFAR-10 dataset. It can be used for image classification tasks on similar datasets.
+
+**Limitations:**
+
+* This model is trained on a relatively small dataset (CIFAR-10) and might not generalize well to unseen data.
+* Training is done without fine-tuning, potentially limiting its performance compared to a fine-tuned model.
+* Training is performed on a single RTX 3090.
+
+## Training Data
+
+The model is trained on the CIFAR-10 dataset, containing 60,000 32x32 color images in 10 classes. 
+
+* Training set: 50,000 images
+* Test set: 10,000 images
+
+**Data Source:** [https://paperswithcode.com/dataset/cifar-10](https://paperswithcode.com/dataset/cifar-10)
+
+## Documentation
+
+* GitHub Repository: [ViT-Classification-CIFAR10](https://github.com/nick8592/ViT-Classification-CIFAR10.git)