Update README.md

README.md

@@ -1,16 +1,34 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- image-classificaiton
+- vit
+- pytorch
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+- f1
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Umsakwa/Uddayvit-image-classification-model
+
+This is a Vision Transformer (ViT)-based model fine-tuned for **image classification tasks**. It classifies images into predefined categories and is suitable for various real-world use cases, including object detection, plant disease identification, and more.
+
 
 
 
 ## Model Details
 
+- **Model Architecture**: Vision Transformer (ViT)
+- **Framework**: PyTorch
+- **Training Data**: The model was trained on [Your Dataset Name]. Include details such as the dataset size, number of classes, and source (e.g., public dataset on Hugging Face or custom dataset).
+- **Dataset Link**: [Dataset on Hugging Face](https://huggingface.co/datasets/your-dataset-name)
+- **Input Data**: The model accepts RGB images in standard formats (e.g., JPEG, PNG) and preprocesses them to the required input size (e.g., 224x224).
+- **Preprocessing**: The model uses a processor that tokenizes and normalizes the input images.
+- 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
@@ -57,7 +75,9 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model’s performance is tied to the quality of the training dataset. For datasets significantly different from the one used for training, fine-tuning might be required.
+It is not robust to extreme distortions, occlusions, or very low-resolution images.
+The model may have biases inherited from the dataset.
 
 [More Information Needed]
 
@@ -75,6 +95,22 @@ Use the code below to get started with the model.
 
 ## Training Details
 
+Frameworks Used:
+
+Transformers (Hugging Face)
+PyTorch
+Hyperparameters:
+
+Epochs: 5
+Batch Size: 16
+Learning Rate: 5e-5
+Optimizer: AdamW
+
+Loss Function: Cross-Entropy Loss
+
+Hardware Used:
+
+GPU: NVIDIA Tesla T4
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->