first commit

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import numpy as np
+import os
+
+from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference
+
+models_name = [
+    "VGG16",
+    "ShuffleNetV2",
+    "mobilenet_v2"
+]
+
+colname = "mobilenet_v2"
+
+radio = gr.inputs.Radio(models_name, default="mobilenet_v2", type="value", label=colname)
+print(radio.label)
+
+def predict_image(image):
+    image = np.array(image) / 255
+    image = np.expand_dims(image, axis=0)
+
+    classifier = TorchVisionClassifierInference(
+        model_path = "./models/" + colname + ".pth",
+    )
+
+    pred = classifier.predict(img=image)
+    return pred
+
+# open categories.txt in read mode
+categories = open("categories.txt", "r")
+labels = categories.readline().split(";")
+
+image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
+label = gr.outputs.Label(num_top_classes=len(labels))
+
+samples = ['./samples/basking.jpg', './samples/blacktip.jpg']
+# , './samples/blacktip.jpg', './samples/blue.jpg', './samples/bull.jpg', './samples/hammerhead.jpg',
+#         './samples/lemon.jpg', './samples/mako.jpg', './samples/nurse.jpg', './samples/sand tiger.jpg', './samples/thresher.jpg', 
+#         './samples/tigre.jpg', './samples/whale.jpg', './samples/white.jpg', './samples/whitetip.jpg']
+        
+interface = gr.Interface(
+    fn=predict_image, 
+    inputs=[image, radio], 
+    outputs=label, 
+    capture_session=True, 
+    allow_flagging=False, 
+    examples=samples
+)
+interface.launch()

categories.txt

@@ -0,0 +1 @@
+basking;blacktip;blue;bull;hammerhead;lemon;mako;nurse;sand tiger;thresher;tiger;whale;white;whitetip

models/VGG16.py

@@ -0,0 +1,34 @@
+import tensorflow as tf
+from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
+from tensorflow.keras.layers import Conv2D, Dense, Flatten, GlobalMaxPooling2D
+from tensorflow.keras.layers import Dense, Input, MaxPooling2D
+from tensorflow.keras import Model
+
+
+def VGG16(nbr_class):
+    # 224 224 3
+    img_input = Input(shape=(224,224,3))
+
+    # first convolution
+    x = Conv2D(64, (3,3), activation='relu', padding='same')(img_input)
+    x = Conv2D(64, (3,3), activation='relu', padding='same')(x)
+    x = MaxPooling2D((2,2), strides = (2,2))(x)
+
+    # second convolution
+    x = Conv2D(128, (3,3), activation='relu', padding='same')(x)
+    x = Conv2D(128, (3,3), activation='relu', padding='same')(x)
+    x = MaxPooling2D((2,2), strides = (2,2))(x)
+
+    # third convolution
+    x = Conv2D(256, (3,3), activation='relu', padding='same')(x)
+    x = Conv2D(256, (3,3), activation='relu', padding='same')(x)
+    x = Conv2D(256, (3,3), activation='relu', padding='same')(x)
+    x = MaxPooling2D((2,2), strides = (2,2))(x)
+
+    x = Flatten()(x)
+    x = Dense(1024, activation='relu')(x)
+    x = Dense(1024, activation='relu')(x)
+    x = Dense(nbr_class, activation='softmax')(x)
+
+
+    return Model(img_input, x, name="vgg16")

models/mobilenet_v2/best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:650c8c31d438caf26c768908e204a0fd8ee48af3f22d157e6eb88237dc103828
+size 9235021

models/mobilenet_v2/config.json

@@ -0,0 +1,39 @@
+{
+    "num_classes": 14,
+    "hidden_size": 1280,
+    "id2label": {
+        "0": "basking",
+        "1": "blacktip",
+        "2": "blue",
+        "3": "bull",
+        "4": "hammerhead",
+        "5": "lemon",
+        "6": "mako",
+        "7": "nurse",
+        "8": "sand_tiger",
+        "9": "thresher",
+        "10": "tiger",
+        "11": "whale",
+        "12": "white",
+        "13": "whitetip"
+    },
+    "label2id": {
+        "basking": "0",
+        "blacktip": "1",
+        "blue": "2",
+        "bull": "3",
+        "hammerhead": "4",
+        "lemon": "5",
+        "mako": "6",
+        "nurse": "7",
+        "sand_tiger": "8",
+        "thresher": "9",
+        "tiger": "10",
+        "whale": "11",
+        "white": "12",
+        "whitetip": "13"
+    },
+    "architectures": [
+        "mobilenet_v2"
+    ]
+}

models/modelNet.py

@@ -0,0 +1,19 @@
+
+import tensorflow as tf
+from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
+from tensorflow.keras.layers import Conv2D, Dense, GlobalMaxPooling2D
+from tensorflow.keras.layers import Dense, MaxPooling2D, BatchNormalization
+from tensorflow.keras.models import Sequential
+from tensorflow.keras import Model
+
+
+def modelNet(nbr_class):
+    mobile_net = tf.keras.applications.MobileNetV2(input_shape=(224,224,3), include_top=False)
+    mobile_net.trainable=False
+  
+    model = Sequential([
+    mobile_net,
+    tf.keras.layers.GlobalAveragePooling2D(),
+    tf.keras.layers.Dense(nbr_class, activation = 'softmax')])
+
+    return model

models/model_v1.py

@@ -0,0 +1,47 @@
+
+import tensorflow as tf
+from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
+from tensorflow.keras.layers import Conv2D, Dense, GlobalMaxPooling2D
+from tensorflow.keras.layers import Dense, MaxPooling2D, BatchNormalization
+from tensorflow.keras.models import Sequential
+from tensorflow.keras import Model
+
+def model_v1(nbr_class):
+    model = Sequential()
+    model.add(Conv2D(64,(3,3), activation="relu", input_shape=(224,224,3)))
+    model.add(BatchNormalization())
+    model.add(Conv2D(64,(3,3), activation="relu"))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D())
+
+    model.add(Conv2D(128,(3,3), activation="relu"))
+    model.add(BatchNormalization())
+    model.add(Conv2D(128,(3,3), activation="relu"))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D())
+
+    model.add(Conv2D(256,(3,3), activation="relu"))
+    model.add(BatchNormalization())
+    model.add(Conv2D(256,(3,3), activation="relu"))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D())
+
+    # model.add(Conv2D(512,(3,3), activation="relu"))
+    # model.add(BatchNormalization())
+    # model.add(Conv2D(512,(3,3), activation="relu"))
+    # model.add(BatchNormalization())
+    # model.add(MaxPooling2D())
+
+    # model.add(Conv2D(512,(3,3), activation="relu"))
+    # model.add(BatchNormalization())
+    # model.add(Conv2D(512,(3,3), activation="relu"))
+    # model.add(BatchNormalization())
+    # model.add(Conv2D(512,(3,3), activation="relu"))
+    # model.add(BatchNormalization())
+    # model.add(GlobalMaxPooling2D())
+
+    model.add(Dense(1024, activation="relu"))
+    model.add(BatchNormalization())
+    model.add(Dense(nbr_class, activation="softmax"))
+
+    return model