update

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,37 +0,0 @@
----
-title: Shark Classifier Mobilenet_v2
-emoji: 🦈
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

app.py

@@ -1,41 +0,0 @@
-import gradio as gr
-import numpy as np
-import pandas as pd
-from tensorflow.keras import models 
-
-import tensorflow as tf
-
-# open categories.txt in read mode
-categories = open("categories.txt", "r")
-labels = categories.readline().split(";")
-
-model = models.load_model('models/modelnet/best_model.h5')
-
-
-def predict_image(image):
-    image = np.array(image) / 255
-    image = np.expand_dims(image, axis=0)
-
-    pred = model.predict(image)
-
-    acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
-    print(acc)
-    return acc
-
-
-image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
-label = gr.outputs.Label(num_top_classes=len(labels))
-
-samples = ['samples/basking.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, 
-    outputs=label, 
-    capture_session=True, 
-    allow_flagging=False, 
-    examples=samples
-)
-interface.launch()

categories.txt

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

models/VGG16.py

@@ -1,34 +0,0 @@
-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/modelNet.py

@@ -1,19 +0,0 @@
-
-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

@@ -1,47 +0,0 @@
-
-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

models/{mobilenet_v2/best_model.pth → modelnet/best_model.h5}

@@ -1,3 +1,3 @@
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-size 9235021
+oid sha256:e7c1ec8dba0efbf87bd07ad89555987bd7be807efbc77d42cab2d0c75a45d6a0
+size 9556672

requirements.txt

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-gradio
-torch
-torchvision
-Pillow
-gdown
-numpy
-scipy
-cmake
-onnxruntime-gpu
-opencv-python-headless
-encoded-video
-hugsvision
-tensorflow