zihaoz96 commited on
Commit
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1 Parent(s): b9f412e
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README.md DELETED
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- ---
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- title: Shark Classifier Mobilenet_v2
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- emoji: 🦈
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- colorFrom: indigo
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- colorTo: green
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- sdk: gradio
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- app_file: app.py
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- pinned: false
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- ---
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-
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- # Configuration
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-
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- `title`: _string_
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- Display title for the Space
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-
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- `emoji`: _string_
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- Space emoji (emoji-only character allowed)
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-
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- `colorFrom`: _string_
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- Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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-
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- `colorTo`: _string_
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- Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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-
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- `sdk`: _string_
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- Can be either `gradio` or `streamlit`
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-
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- `sdk_version` : _string_
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- Only applicable for `streamlit` SDK.
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- See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
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-
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- `app_file`: _string_
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- Path to your main application file (which contains either `gradio` or `streamlit` Python code).
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- Path is relative to the root of the repository.
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-
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- `pinned`: _boolean_
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- Whether the Space stays on top of your list.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app.py DELETED
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- import gradio as gr
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- import numpy as np
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- import pandas as pd
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- from tensorflow.keras import models
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-
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- import tensorflow as tf
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-
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- # open categories.txt in read mode
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- categories = open("categories.txt", "r")
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- labels = categories.readline().split(";")
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-
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- model = models.load_model('models/modelnet/best_model.h5')
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-
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-
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- def predict_image(image):
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- image = np.array(image) / 255
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- image = np.expand_dims(image, axis=0)
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-
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- pred = model.predict(image)
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-
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- acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
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- print(acc)
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- return acc
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-
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-
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- image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
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- label = gr.outputs.Label(num_top_classes=len(labels))
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-
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- samples = ['samples/basking.jpg', 'samples/blacktip.jpg', 'samples/blue.jpg', 'samples/bull.jpg', 'samples/hammerhead.jpg',
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- 'samples/lemon.jpg', 'samples/mako.jpg', 'samples/nurse.jpg', 'samples/sand tiger.jpg', 'samples/thresher.jpg',
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- 'samples/tigre.jpg', 'samples/whale.jpg', 'samples/white.jpg', 'samples/whitetip.jpg']
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-
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- interface = gr.Interface(
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- fn=predict_image,
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- inputs=image,
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- outputs=label,
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- capture_session=True,
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- allow_flagging=False,
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- examples=samples
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- )
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- interface.launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
categories.txt DELETED
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- basking;blacktip;blue;bull;hammerhead;lemon;mako;nurse;sand tiger;thresher;tiger;whale;white;whitetip
 
 
models/VGG16.py DELETED
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- import tensorflow as tf
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- from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
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- from tensorflow.keras.layers import Conv2D, Dense, Flatten, GlobalMaxPooling2D
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- from tensorflow.keras.layers import Dense, Input, MaxPooling2D
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- from tensorflow.keras import Model
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-
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-
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- def VGG16(nbr_class):
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- # 224 224 3
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- img_input = Input(shape=(224,224,3))
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-
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- # first convolution
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- x = Conv2D(64, (3,3), activation='relu', padding='same')(img_input)
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- x = Conv2D(64, (3,3), activation='relu', padding='same')(x)
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- x = MaxPooling2D((2,2), strides = (2,2))(x)
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-
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- # second convolution
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- x = Conv2D(128, (3,3), activation='relu', padding='same')(x)
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- x = Conv2D(128, (3,3), activation='relu', padding='same')(x)
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- x = MaxPooling2D((2,2), strides = (2,2))(x)
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-
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- # third convolution
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- x = Conv2D(256, (3,3), activation='relu', padding='same')(x)
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- x = Conv2D(256, (3,3), activation='relu', padding='same')(x)
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- x = Conv2D(256, (3,3), activation='relu', padding='same')(x)
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- x = MaxPooling2D((2,2), strides = (2,2))(x)
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-
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- x = Flatten()(x)
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- x = Dense(1024, activation='relu')(x)
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- x = Dense(1024, activation='relu')(x)
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- x = Dense(nbr_class, activation='softmax')(x)
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-
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-
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- return Model(img_input, x, name="vgg16")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
models/modelNet.py DELETED
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-
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- import tensorflow as tf
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- from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
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- from tensorflow.keras.layers import Conv2D, Dense, GlobalMaxPooling2D
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- from tensorflow.keras.layers import Dense, MaxPooling2D, BatchNormalization
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- from tensorflow.keras.models import Sequential
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- from tensorflow.keras import Model
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-
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-
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- def modelNet(nbr_class):
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- mobile_net = tf.keras.applications.MobileNetV2(input_shape=(224,224,3), include_top=False)
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- mobile_net.trainable=False
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-
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- model = Sequential([
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- mobile_net,
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- tf.keras.layers.GlobalAveragePooling2D(),
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- tf.keras.layers.Dense(nbr_class, activation = 'softmax')])
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-
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- return model
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
models/model_v1.py DELETED
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-
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- import tensorflow as tf
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- from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
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- from tensorflow.keras.layers import Conv2D, Dense, GlobalMaxPooling2D
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- from tensorflow.keras.layers import Dense, MaxPooling2D, BatchNormalization
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- from tensorflow.keras.models import Sequential
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- from tensorflow.keras import Model
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-
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- def model_v1(nbr_class):
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- model = Sequential()
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- model.add(Conv2D(64,(3,3), activation="relu", input_shape=(224,224,3)))
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- model.add(BatchNormalization())
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- model.add(Conv2D(64,(3,3), activation="relu"))
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- model.add(BatchNormalization())
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- model.add(MaxPooling2D())
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-
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- model.add(Conv2D(128,(3,3), activation="relu"))
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- model.add(BatchNormalization())
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- model.add(Conv2D(128,(3,3), activation="relu"))
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- model.add(BatchNormalization())
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- model.add(MaxPooling2D())
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-
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- model.add(Conv2D(256,(3,3), activation="relu"))
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- model.add(BatchNormalization())
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- model.add(Conv2D(256,(3,3), activation="relu"))
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- model.add(BatchNormalization())
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- model.add(MaxPooling2D())
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-
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- # model.add(Conv2D(512,(3,3), activation="relu"))
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- # model.add(BatchNormalization())
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- # model.add(Conv2D(512,(3,3), activation="relu"))
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- # model.add(BatchNormalization())
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- # model.add(MaxPooling2D())
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-
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- # model.add(Conv2D(512,(3,3), activation="relu"))
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- # model.add(BatchNormalization())
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- # model.add(Conv2D(512,(3,3), activation="relu"))
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- # model.add(BatchNormalization())
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- # model.add(Conv2D(512,(3,3), activation="relu"))
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- # model.add(BatchNormalization())
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- # model.add(GlobalMaxPooling2D())
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-
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- model.add(Dense(1024, activation="relu"))
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- model.add(BatchNormalization())
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- model.add(Dense(nbr_class, activation="softmax"))
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-
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- return model
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
models/{mobilenet_v2/best_model.pth → modelnet/best_model.h5} RENAMED
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requirements.txt DELETED
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- gradio
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- torch
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- torchvision
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- Pillow
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- gdown
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- numpy
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- scipy
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- cmake
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- onnxruntime-gpu
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- opencv-python-headless
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- encoded-video
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- hugsvision
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- tensorflow
 
 
 
 
 
 
 
 
 
 
 
 
 
 
samples/basking.jpg DELETED
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samples/blacktip.jpg DELETED
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samples/blue.jpg DELETED
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samples/bull.jpg DELETED
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samples/hammerhead.jpg DELETED
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samples/lemon.jpg DELETED
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samples/mako.jpg DELETED
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samples/nurse.jpg DELETED
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samples/sand tiger.jpg DELETED
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samples/thresher.jpg DELETED
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samples/tigre.jpg DELETED
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samples/whale.jpg DELETED
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samples/white.jpg DELETED
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samples/whitetip.jpg DELETED
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