Update README.md

Fixed Readme: Usage and Cloning

README.md

@@ -29,28 +29,36 @@ This model is a deep learning-based classifier designed to detect and classify d
 The model aims to assist in early diagnosis and grading of diabetic retinopathy, reducing the workload for ophthalmologists and improving accessibility to screening.
 
 ## Usage
-You can use this model via the Hugging Face `transformers` or `torch` library for inference.
+You can use this model by cloning the repository and using the pickled model by <i>torch.load()</i>.
 
-### Installation
+### Dependencies Installation
 Ensure you have the required dependencies installed:
 ```bash
 pip install torch torchvision transformers opencv-python pandas
 ```
 
 ### Loading the Model
+
+Clone the repository (with GIT LFS enabled)
+```bash
+git lfs install
+
+git clone https://huggingface.co/sakshamkr1/ResNet50-APTOS-DR
+```
+
+Load the Model
 ```python
 import torch
-from torchvision import transforms
 from PIL import Image
-from transformers import AutoModel
 
-# Load model
-model = AutoModel.from_pretrained("your-huggingface-username/model-name")
+model = torch.load(model_path, map_location=torch.device('gpu'), weights_only=False) #Change torch.device to 'cpu' if using CPU
 model.eval()
 ```
 
 ### Transformer Application
 ```python
+from torchvision import transforms
+
 transform = transforms.Compose([
     transforms.Resize((224, 224)),  # Resize image to match input size
     transforms.ToTensor(),  # Convert image to tensor
@@ -60,6 +68,8 @@ transform = transforms.Compose([
 
 ### Function to preprocess image and get predictions
 ```python
+import numpy as np
+
 def predict(image_path):
     # Load and preprocess the input image
     image = Image.open(image_path).convert('RGB')  # Ensure RGB format