Add VQA Examples

apps/article.py

@@ -66,4 +66,206 @@ def app(state=None):
     
     toc.header("Acknowledgements")
     st.write(read_markdown("acknowledgements.md"))
+
+    toc.header("VQA Examples")
+    toc.subheader("Color Questions")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+
+    col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
+    col1.write("**Custom Question**: What color are the horses?")
+    col1.write("**Predicted Answer**: brown✅")
+
+    col2.image("./sections/examples/cat_color.jpeg", use_column_width="auto", width=300)
+    col2.write("**Custom Question**: What color is the cat?")
+    col2.write("**Predicted Answer**: white✅")
+
+    col3.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
+    col3.write("**Custom Question**: What color is the man's jacket?")
+    col3.write("**Predicted Answer**: black⚫")
+
+    col1.image("./sections/examples/car_color.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: What color is the car?")
+    col1.write("**Predicted Answer**: blue❎")
+
+    col2.image("./sections/examples/coat_color.jpeg", use_column_width="auto", width=300)
+    col2.write("**Actual Question**: What color is this person's coat?")
+    col2.write("**Predicted Answer**: blue✅")
+
+    toc.subheader("Counting Questions")
+
+    col1, col2, col3 = st.beta_columns([1,1, 1])
+
+    col1.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: How many zebras are there?")
+    col1.write("**Predicted Answer**: 0❎")
+
+    col2.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
+    col2.write("**Custom Question**: How many giraffes are there?")
+    col2.write("**Predicted Answer**: 2❎")
+
+    col3.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
+    col3.write("**Custom Question**: How many teddy bears are present in the image?")
+    col3.write("**Predicted Answer**: 3✅")
+
+    col1.image("./sections/examples/candle_count.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: ¿Cuantas velas hay en el cupcake?")
+    col1.write("**English Translation**: How many candles are in the cupcake?")
+    col1.write("**Predicted Answer**: 0❎")
+
+    col1.image("./sections/examples/people_picture.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: ¿A cuánta gente le están tomando una foto?")
+    col1.write("**English Translation**: How many people are you taking a picture of?")
+    col1.write("**Predicted Answer**: 10❎")
+
+    toc.subheader("Size/Shape Questions")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+    col1.image("./sections/examples/vase.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**:  What shape is the vase? ")
+    col1.write("**Predicted Answer**: round✅")
+    
+
+    toc.subheader("Yes/No Questions")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+
+    col1.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: Sind das drei Teddybären?")
+    col1.write("**English Translation**: Are those teddy bears?")
+    col1.write("**Predicted Answer**: Ja (yes)✅")
+
+    col2.image("./sections/examples/winter.jpeg", use_column_width="auto", width=300)
+    col2.write("**Actual Question**: ¿Se lo tomaron en invierno?")
+    col2.write("**English Translation**: Did they take it in winter?")
+    col2.write("**Predicted Answer**: si (yes)✅")
+
+    col3.image("./sections/examples/clock.jpeg", use_column_width="auto", width=300)
+    col3.write("**Actual Question**: Is the clock ornate? ")
+    col3.write("**Predicted Answer**: yes✅")
+
+    col1.image("./sections/examples/decorated_building.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: Ist das Gebäude orniert?")
+    col1.write("**English Translation**: Is the building decorated?")
+    col1.write("**Predicted Answer**: Ja (yes)✅")
+    
+    col2.image("./sections/examples/commuter_train.jpeg", use_column_width="auto", width=300)
+    col2.write("**Actual Question**: Ist das ein Pendler-Zug?")
+    col2.write("**English Translation**: Is that a commuter train?")
+    col2.write("**Predicted Answer**: Ja (yes)❎")
+
+    col3.image("./sections/examples/is_in_a_restaurant.jpeg", use_column_width="auto", width=300)
+    col3.write("**Actual Question**: Elle est dans un restaurant?")
+    col3.write("**English Translation**: Is she in a restaurant?")
+    col3.write("**Predicted Answer**: Oui (yes)❎")
+
+    col1.image("./sections/examples/giraffe_eyes.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: Est-ce que l'œil de la girafe est fermé?")
+    col1.write("**English Translation**: Are the giraffe's eyes closed?")
+    col1.write("**Predicted Answer**: Oui (yes)❎")
+
+    toc.subheader("Negatives Test")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+    col1.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
+
+    col2.write("**Actual Question**: Is the man happy?")
+    col2.write("**Predicted Answer**: Yes✅")
+
+    col3.write("**Actual Question**: Is the man not happy?")
+    col3.write("**Predicted Answer**: Yes❎")
+
+    col2.write("**Actual Question**: Is the man sad?")
+    col2.write("**Predicted Answer**: No✅")
+
+    col3.write("**Actual Question**: Is the man not sad?")
+    col3.write("**Predicted Answer**: No❎")
+
+    col2.write("**Actual Question**: Is the man unhappy?")
+    col2.write("**Predicted Answer**: No✅")
+
+    col3.write("**Actual Question**: Is the man not unhappy?")
+    col3.write("**Predicted Answer**: No❎")
+
+    toc.subheader("Multilinguality Test")
+
+    toc.subsubheader("Color Question")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+    col1.image("./sections/examples/truck_color.jpeg", use_column_width="auto", width=300)
+
+    col2.write("**Actual Question**: What color is the building?")
+    col2.write("**Predicted Answer**: red✅")
+
+    col3.write("**Actual Question**: Welche Farbe hat das Gebäude?")
+    col3.write("**English Translation**: What color is the building?")
+    col3.write("**Predicted Answer**: rot (red)✅")
+
+    col2.write("**Actual Question**: ¿De qué color es el edificio?")
+    col2.write("**English Translation**: What color is the building?")
+    col2.write("**Predicted Answer**: rojo (red)✅")
+
+    col3.write("**Actual Question**: De quelle couleur est le bâtiment ?")
+    col3.write("**English Translation**: What color is the building?")
+    col3.write("**Predicted Answer**: rouge (red)✅")
+
+    toc.subsubheader("Counting Question")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+    col1.image("./sections/examples/bear.jpeg", use_column_width="auto", width=300)
+
+    col2.write("**Actual Question**: How many bears do you see?")
+    col2.write("**Predicted Answer**: 1✅")
+
+    col3.write("**Actual Question**: Wie viele Bären siehst du?")
+    col3.write("**English Translation**: How many bears do you see?")
+    col3.write("**Predicted Answer**: 1✅")
+
+    col2.write("**Actual Question**: ¿Cuántos osos ves?")
+    col2.write("**English Translation**: How many bears do you see?")
+    col2.write("**Predicted Answer**: 1✅")
+
+    col3.write("**Actual Question**: Combien d'ours voyez-vous ?")
+    col3.write("**English Translation**: How many bears do you see?")
+    col3.write("**Predicted Answer**: 1✅")
+
+    toc.subsubheader("Misc Question")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+    col1.image("./sections/examples/bench.jpeg", use_column_width="auto", width=300)
+
+    col2.write("**Actual Question**: Where is the bench?")
+    col2.write("**Predicted Answer**: field✅")
+
+    col3.write("**Actual Question**: Où est le banc ?")
+    col3.write("**English Translation**: Where is the bench?")
+    col3.write("**Predicted Answer**: domaine (field)✅")
+
+    col2.write("**Actual Question**: ¿Dónde está el banco?")
+    col2.write("**English Translation**: Where is the bench?")
+    col2.write("**Predicted Answer**: campo (field)✅")
+
+    col3.write("**Actual Question**: Wo ist die Bank?")
+    col3.write("**English Translation**: Where is the bench?")
+    col3.write("**Predicted Answer**: Feld (field)✅")
+
+
+    toc.subheader("Misc Questions")
+    col1, col2, col3 = st.beta_columns([1,1,1])
+
+    col1.image("./sections/examples/tennis.jpeg", use_column_width="auto", width=300)
+    col1.write("**Actual Question**: ¿Qué clase de juego está viendo la multitud?")
+    col1.write("**English Translation**: What kind of game is the crowd watching?")
+    col1.write("**Predicted Answer**: tenis (tennis)✅")
+
+    col2.image("./sections/examples/men_body_suits.jpeg", use_column_width="auto", width=300)
+    col2.write("**Custom Question**: What are the men wearing?")
+    col2.write("**Predicted Answer**: wetsuits✅")
+
+    col3.image("./sections/examples/bathroom.jpeg", use_column_width="auto", width=300)
+    col3.write("**Actual Question**: ¿A qué habitación perteneces?")
+    col3.write("**English Translation**: What room do you belong to?")
+    col3.write("**Predicted Answer**: bano (bathroom)✅")
+
+    col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
+    col1.write("**Custom Question**: What are the men riding?")
+    col1.write("**Predicted Answer**: horses✅")
+
+    col2.image("./sections/examples/inside_outside.jpeg", use_column_width="auto", width=300)
+    col2.write("**Actual Question**: Was this taken inside or outside?")
+    col2.write("**Predicted Answer**: inside✅")
+
     toc.generate()