Update README.md

README.md

@@ -13,17 +13,18 @@ library_name: keras
 panda_cat_dog_classification
 
 ### Model Description
-This model classifies animals among pandas, cats and dogs. It was trained using inceptionV3 model. 
+This model classifies animals among pandas, cats and dogs. It was trained using custom CNN model. 
 
 
 - **Developed by:** Neelima Monjusha Preeti
-- **Model type:** inceptionV3
+- **Model type:** custom CNN model
 - **Language(s):** Python
 - **License:** MIT
 - **Contact:** [email protected]
 
 ### Data Preprocessing
 The image dataset is preprocessed with the following portion:
+
 ```bash
 transform = transforms.Compose([
   transforms.Resize((224,224)),
@@ -65,10 +66,87 @@ class CNN(nn.Module):
 ```
 Then used batch_size = 8 and CrossEntropyLoss() for loss function. Then used Adam optimizer with a learning rate 0.001 for optimization process.
 
+```bash
+loss_function = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+```
+### Training Loop
+
+Loading the data then breaking it into mini batches. Then forward pass and loss function calculation. After that backward propagation and optimization.
+Backward Propagation and Optimization:
+
+```bash
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+```
+### Test data
+
+Test data loaded and calculate the accuracy.
+
+The accuracy was 53.333333333333336% . 
+
+### Result Analysis
+The packages needed for creating the huggingface interface is loaded with:
+
+```bash
+import gradio as gr
+import torch
+from torchvision import transforms
+```
+
+ The model was saved with the following:
 
+ ```bash
+model_scripted = torch.jit.script(model)
+model_scripted.save('./models/cat_dog_cnn.pt')
+```
+## HuggingFace Result analysis
+First the custom model cat_dog_cnn.pt is loaded. Then the output function is specified. As this is a Image Classification model.
+
+```bash
+|---app_data
+|      |---cat.jpg
+|      |---dog.jpg
+|      |---panda.jpg
+|
+```
+
+Example images are loaded. 
+The classes for prediction are - CLASSES = ["Cat", "Dog", "Panda"].
+The output function for prediction is 
+
+```bash
+def classify_image(inp):
+  inp = transform(inp).unsqueeze(0)
+  out = model(inp)
+  return CLASSES[out.argmax().item()]
+```
 
+This will return the classes of the input image.
+# Interface Creation
 
+For creating huggingface interface this following portion is added:
 
+```bash
+iface = gr.Interface(fn=classify_image,
+                     inputs=gr.Image(type="pil", label="Input Image"),
+                     outputs="text",
+                     examples=[
+
+                       "./app_data/cat.jpg",
+                       "./app_data/dog.jpg",
+                       "./app_data/panda.jpg",
+                     
+                               
+                     ])
+```
+This portion is going to create an interface for taking the image input. Then example images and output is defined to be the classes from cat, dog and panda.
+Now with the following the interface of the app is loaded. 
+
+```bash
+iface.launch()
+```
 
 
 ### Project Structure