Update app.py

app.py

@@ -50,13 +50,23 @@ st.markdown("4. When giving the options ['Golden Retriever is running', 'Golden
 figure7 = Image.open('Result4.png')
 st.image(figure7, caption="When giving the options ['Golden Retriever is running', 'Golden Retriever is playing', 'Golden Retriever is sitting', 'Golden Retriever is sleeping', 'Siberian Husky is running', 'Siberian Husky is playing', 'Siberian Husky is sitting', 'Siberian Husky is sleeping', 'Labrador is running', 'Labrador is playing', 'Labrador is sitting', 'Labrador is sleeping', 'Boxer is running', 'Boxer is playing', 'Boxer is sitting', 'Boxer is sleeping']")
 
-st.markdown("## Animal Classfication")
+st.markdown("## Animal Classification")
+
+st.markdown("1. We carried out the classification of four kinds of animals containing buffalo, elephant, rhino, and zebra. There are 1000 images for each kind of animal. In this part, we use CLIP to classify four kinds of animals and the accuracy is 0.9830.")
+
+figure8 = Image.open('conf1.png')
+st.image(figure8, caption='The confusion matrix of four kinds of animal classification')
+
+st.markdown("2. In order to test the performance of CLIP in the fine-grained classification task, we added six other animals to the original dataset of four animals. In the meanwhile, the dataset becomes an unbalanced dataset. Some animal species have more than 4000 images but someone just has 1000 images. Finally, the accuracy of animals is 0.9592")
+
+figure9 = Image.open('conf2.png')
+st.image(figure9, caption='The confusion matrix of ten kinds of animal classification')
 
 st.markdown("## Critical thinking")
 
 st.markdown("1. The performance of CLIP is depend significantly on class design and the choices one makes for categories. Different class designs have a great influence on the final result")
 
-st.markdown("2. The model can identify people's race, gender, age, beauty, and ugliness. It may describe specific groups unfriendly, such as short,and so on. These identifications of characters may bring potential discrimination.")
+st.markdown("2. The model can identify people's race, gender, age, beauty, and ugliness. It may describe specific groups discriminatively, such as short, and so on. These identifications of characters may bring potential discrimination.")
 
 st.markdown("3. The model still requires us to give some possible options, which cannot be described directly from the picture. At the same time, the accuracy of the model on fine-grained classification tasks did not meet expectations")