
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
This model is built for a coding challenge for Fatima Fellowship 2023 Application. It is for classifing an image as ai generated or real.
## Model Details
The model was built using ResNet50 pretrained. The skeleton of the model is
base = ResNet50(include_top=False, weights='imagenet', input_shape=(128,128,3))
for layer in base.layers:
    layer.trainable = False
output = base.output
output = Flatten()(output)
output = Dense(256, activation='relu')(output)
output = Dense(128, activation='relu')(output)
output = Dropout(rate=0.3)(output)
output = Dense(1, activation = "sigmoid")(output)
model = Model(base.input, output)


### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [Mizanu Zelalem]


### Model Sources [optional]

<!-- Provide the basic links for the model. -->
  - **Repository:**
- Colab notebook of the project can be found here : https://colab.research.google.com/drive/1-50OF83EwzezdGAV0Tna_b5gvC7qjSi3?usp=sharing
- model.h5 can be accesed from here : https://drive.google.com/file/d/10aqeUV6RkhnSRuc1OLKl0uOMXdo9DouQ/view?usp=sharing




{}
region:us
TAGS
#region-us 


# Model Card for Model ID


This model is built for a coding challenge for Fatima Fellowship 2023 Application. It is for classifing an image as ai generated or real.
## Model Details
The model was built using ResNet50 pretrained. The skeleton of the model is
base = ResNet50(include_top=False, weights='imagenet', input_shape=(128,128,3))
for layer in URL:
    layer.trainable = False
output = URL
output = Flatten()(output)
output = Dense(256, activation='relu')(output)
output = Dense(128, activation='relu')(output)
output = Dropout(rate=0.3)(output)
output = Dense(1, activation = "sigmoid")(output)
model = Model(URL, output)


### Model Description





- Developed by: [Mizanu Zelalem]


### Model Sources [optional]


  - Repository:
- Colab notebook of the project can be found here : URL
- model.h5 can be accesed from here : URL




# Model Card for Model ID


This model is built for a coding challenge for Fatima Fellowship 2023 Application. It is for classifing an image as ai generated or real. ## Model Details
The model was built using ResNet50 pretrained. The skeleton of the model is
base = ResNet50(include_top=False, weights='imagenet', input_shape=(128,128,3))
for layer in URL:
    layer.trainable = False
output = URL
output = Flatten()(output)
output = Dense(256, activation='relu')(output)
output = Dense(128, activation='relu')(output)
output = Dropout(rate=0.3)(output)
output = Dense(1, activation = "sigmoid")(output)
model = Model(URL, output) ### Model Description





- Developed by: [Mizanu Zelalem] ### Model Sources [optional]


  - Repository:
- Colab notebook of the project can be found here : URL
- model.h5 can be accesed from here : URL
TAGS
#region-us 
 # Model Card for Model ID


This model is built for a coding challenge for Fatima Fellowship 2023 Application. It is for classifing an image as ai generated or real. ## Model Details
The model was built using ResNet50 pretrained. The skeleton of the model is
base = ResNet50(include_top=False, weights='imagenet', input_shape=(128,128,3))
for layer in URL:
    layer.trainable = False
output = URL
output = Flatten()(output)
output = Dense(256, activation='relu')(output)
output = Dense(128, activation='relu')(output)
output = Dropout(rate=0.3)(output)
output = Dense(1, activation = "sigmoid")(output)
model = Model(URL, output) ### Model Description





- Developed by: [Mizanu Zelalem] ### Model Sources [optional]


  - Repository:
- Colab notebook of the project can be found here : URL
- model.h5 can be accesed from here : URL
TAGS
#region-us 
# Model Card for Model ID


This model is built for a coding challenge for Fatima Fellowship 2023 Application. It is for classifing an image as ai generated or real.## Model Details
The model was built using ResNet50 pretrained. The skeleton of the model is
base = ResNet50(include_top=False, weights='imagenet', input_shape=(128,128,3))
for layer in URL:
    layer.trainable = False
output = URL
output = Flatten()(output)
output = Dense(256, activation='relu')(output)
output = Dense(128, activation='relu')(output)
output = Dropout(rate=0.3)(output)
output = Dense(1, activation = "sigmoid")(output)
model = Model(URL, output)### Model Description





- Developed by: [Mizanu Zelalem]### Model Sources [optional]


  - Repository:
- Colab notebook of the project can be found here : URL
- model.h5 can be accesed from here : URL