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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+incept_v3_10fps_full_dp0.4.keras filter=lfs diff=lfs merge=lfs -text
+res10_300x300_ssd_iter_140000.caffemodel filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,132 @@
+import os
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+import streamlit as st
+import cv2
+from tqdm import tqdm
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+from tempfile import NamedTemporaryFile
+from functions import *
+au_to_movements= {
+    'au1': 'inner brow raiser',
+    'au2': 'outer brow raiser',
+    'au4': 'brow lowerer',
+    'au5': 'upper lid raiser',
+    'au6': 'cheek raiser',
+    'au9': 'nose wrinkler',
+    'au12': 'lip corner puller',
+    'au15': 'lip corner depressor',
+    'au17': 'chin raiser',
+    'au20': 'lip stretcher',
+    'au25': 'lips part',
+    'au26': 'jaw drop'
+}
+au_labels = [
+    "au1",
+    "au12",
+    "au15",
+    "au17",
+    "au2",
+    "au20",
+    "au25",
+    "au26",
+    "au4",
+    "au5",
+    "au6",
+    "au9"
+]
+col=[au_to_movements[i] for i in au_labels]
+def binary_focal_loss(gamma=2.0, alpha=0.25):
+    def focal_loss(y_true, y_pred):
+        # Define epsilon to avoid log(0)
+        epsilon = tf.keras.backend.epsilon()
+        # Clip predictions to prevent log(0) and log(1 - 0)
+        y_pred = tf.clip_by_value(y_pred, epsilon, 1.0 - epsilon)
+        # Compute the focal loss
+        fl = - alpha * (y_true * (1 - y_pred)**gamma * tf.math.log(y_pred)
+                       + (1 - y_true) * (y_pred**gamma) * tf.math.log(1 - y_pred))
+        return tf.reduce_mean(fl, axis=-1)
+    return focal_loss
+
+loss = binary_focal_loss(gamma=2.0, alpha=0.25)
+
+# Function to read video frames into a list
+def read_video_frames(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frames.append(frame)
+
+    cap.release()
+    return frames
+
+# Function to process frames and make predictions
+def process_frames(frames, model):
+    frames = [get_face(frame) for frame in tqdm(frames)]
+    st.text(f"face shape : {frames[0].shape}")
+    frame_array = np.array(frames)
+    preds = model.predict(frame_array).round()
+    return preds
+
+# Function to save predictions to a CSV file
+def save_predictions_to_csv(predictions, filename="predictions.csv"):
+    df = pd.DataFrame(predictions,columns=col)
+    df.to_csv(filename, index=False)
+    return filename
+
+# Load your Keras model
+def load_model():
+    model = tf.keras.models.load_model('incept_v3_10fps_full_dp0.2.keras',
+                                       custom_objects={'binary_focal_loss': binary_focal_loss})
+    return model
+
+# Streamlit app
+def main():
+    st.title("Video Frame Prediction App")
+    
+    # Upload video file
+    uploaded_file = st.file_uploader("Upload a video file", type=["mp4", "avi", "mov"])
+
+    if uploaded_file is not None:
+        with NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            video_path = tmp_file.name
+
+        # Load the model
+        model = load_model()
+
+        # Predict button
+        if st.button("Predict"):
+            # Read frames from video
+            st.text("Reading video frames...")
+            frames = read_video_frames(video_path)
+            st.text(f"Total frames read: {len(frames)}")
+
+            # Process frames and make predictions
+            st.text("Processing frames and making predictions...")
+            predictions = process_frames(frames, model)
+            st.text("Predictions completed!")
+
+            # Save predictions to CSV
+            csv_file_path = save_predictions_to_csv(predictions)
+            st.text("Predictions saved to CSV!")
+
+            # Make CSV downloadable
+            with open(csv_file_path, "rb") as f:
+                st.download_button(
+                    label="Download CSV",
+                    data=f,
+                    file_name="predictions.csv",
+                    mime="text/csv"
+                )
+
+            # Clean up the temporary file
+            os.remove(video_path)
+
+if __name__ == "__main__":
+    main()

deploy.prototxt

@@ -0,0 +1,1789 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 300
+  dim: 300
+}
+
+layer {
+  name: "data_bn"
+  type: "BatchNorm"
+  bottom: "data"
+  top: "data_bn"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "data_scale"
+  type: "Scale"
+  bottom: "data_bn"
+  top: "data_bn"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_h"
+  type: "Convolution"
+  bottom: "data_bn"
+  top: "conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "msra"
+      variance_norm: FAN_OUT
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "conv1_bn_h"
+  type: "BatchNorm"
+  bottom: "conv1_h"
+  top: "conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "conv1_scale_h"
+  type: "Scale"
+  bottom: "conv1_h"
+  top: "conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_relu"
+  type: "ReLU"
+  bottom: "conv1_h"
+  top: "conv1_h"
+}
+layer {
+  name: "conv1_pool"
+  type: "Pooling"
+  bottom: "conv1_h"
+  top: "conv1_pool"
+  pooling_param {
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "layer_64_1_conv1_h"
+  type: "Convolution"
+  bottom: "conv1_pool"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_64_1_bn2_h"
+  type: "BatchNorm"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_64_1_scale2_h"
+  type: "Scale"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_64_1_relu2"
+  type: "ReLU"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+}
+layer {
+  name: "layer_64_1_conv2_h"
+  type: "Convolution"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv2_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_64_1_sum"
+  type: "Eltwise"
+  bottom: "layer_64_1_conv2_h"
+  bottom: "conv1_pool"
+  top: "layer_64_1_sum"
+}
+layer {
+  name: "layer_128_1_bn1_h"
+  type: "BatchNorm"
+  bottom: "layer_64_1_sum"
+  top: "layer_128_1_bn1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_128_1_scale1_h"
+  type: "Scale"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_bn1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_128_1_relu1"
+  type: "ReLU"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_bn1_h"
+}
+layer {
+  name: "layer_128_1_conv1_h"
+  type: "Convolution"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_bn2"
+  type: "BatchNorm"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_128_1_scale2"
+  type: "Scale"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_128_1_relu2"
+  type: "ReLU"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+}
+layer {
+  name: "layer_128_1_conv2"
+  type: "Convolution"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_conv_expand_h"
+  type: "Convolution"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_conv_expand_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_sum"
+  type: "Eltwise"
+  bottom: "layer_128_1_conv2"
+  bottom: "layer_128_1_conv_expand_h"
+  top: "layer_128_1_sum"
+}
+layer {
+  name: "layer_256_1_bn1"
+  type: "BatchNorm"
+  bottom: "layer_128_1_sum"
+  top: "layer_256_1_bn1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_256_1_scale1"
+  type: "Scale"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_bn1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_256_1_relu1"
+  type: "ReLU"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_bn1"
+}
+layer {
+  name: "layer_256_1_conv1"
+  type: "Convolution"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_bn2"
+  type: "BatchNorm"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_256_1_scale2"
+  type: "Scale"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_256_1_relu2"
+  type: "ReLU"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+}
+layer {
+  name: "layer_256_1_conv2"
+  type: "Convolution"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_conv_expand"
+  type: "Convolution"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_conv_expand"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_sum"
+  type: "Eltwise"
+  bottom: "layer_256_1_conv2"
+  bottom: "layer_256_1_conv_expand"
+  top: "layer_256_1_sum"
+}
+layer {
+  name: "layer_512_1_bn1"
+  type: "BatchNorm"
+  bottom: "layer_256_1_sum"
+  top: "layer_512_1_bn1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_512_1_scale1"
+  type: "Scale"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_bn1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_512_1_relu1"
+  type: "ReLU"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_bn1"
+}
+layer {
+  name: "layer_512_1_conv1_h"
+  type: "Convolution"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1 # 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_bn2_h"
+  type: "BatchNorm"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_512_1_scale2_h"
+  type: "Scale"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_512_1_relu2"
+  type: "ReLU"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+}
+layer {
+  name: "layer_512_1_conv2_h"
+  type: "Convolution"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv2_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 2 # 1
+    kernel_size: 3
+    stride: 1
+    dilation: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_conv_expand_h"
+  type: "Convolution"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_conv_expand_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1 # 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_sum"
+  type: "Eltwise"
+  bottom: "layer_512_1_conv2_h"
+  bottom: "layer_512_1_conv_expand_h"
+  top: "layer_512_1_sum"
+}
+layer {
+  name: "last_bn_h"
+  type: "BatchNorm"
+  bottom: "layer_512_1_sum"
+  top: "layer_512_1_sum"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "last_scale_h"
+  type: "Scale"
+  bottom: "layer_512_1_sum"
+  top: "layer_512_1_sum"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "last_relu"
+  type: "ReLU"
+  bottom: "layer_512_1_sum"
+  top: "fc7"
+}
+
+layer {
+  name: "conv6_1_h"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "conv6_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_1_relu"
+  type: "ReLU"
+  bottom: "conv6_1_h"
+  top: "conv6_1_h"
+}
+layer {
+  name: "conv6_2_h"
+  type: "Convolution"
+  bottom: "conv6_1_h"
+  top: "conv6_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_relu"
+  type: "ReLU"
+  bottom: "conv6_2_h"
+  top: "conv6_2_h"
+}
+layer {
+  name: "conv7_1_h"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv7_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_1_relu"
+  type: "ReLU"
+  bottom: "conv7_1_h"
+  top: "conv7_1_h"
+}
+layer {
+  name: "conv7_2_h"
+  type: "Convolution"
+  bottom: "conv7_1_h"
+  top: "conv7_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_relu"
+  type: "ReLU"
+  bottom: "conv7_2_h"
+  top: "conv7_2_h"
+}
+layer {
+  name: "conv8_1_h"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv8_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_1_relu"
+  type: "ReLU"
+  bottom: "conv8_1_h"
+  top: "conv8_1_h"
+}
+layer {
+  name: "conv8_2_h"
+  type: "Convolution"
+  bottom: "conv8_1_h"
+  top: "conv8_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_relu"
+  type: "ReLU"
+  bottom: "conv8_2_h"
+  top: "conv8_2_h"
+}
+layer {
+  name: "conv9_1_h"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv9_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_1_relu"
+  type: "ReLU"
+  bottom: "conv9_1_h"
+  top: "conv9_1_h"
+}
+layer {
+  name: "conv9_2_h"
+  type: "Convolution"
+  bottom: "conv9_1_h"
+  top: "conv9_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_relu"
+  type: "ReLU"
+  bottom: "conv9_2_h"
+  top: "conv9_2_h"
+}
+layer {
+  name: "conv4_3_norm"
+  type: "Normalize"
+  bottom: "layer_256_1_bn1"
+  top: "conv4_3_norm"
+  norm_param {
+    across_spatial: false
+    scale_filler {
+      type: "constant"
+      value: 20
+    }
+    channel_shared: false
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_loc"
+  top: "conv4_3_norm_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_loc_perm"
+  top: "conv4_3_norm_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_conf"
+  top: "conv4_3_norm_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_conf_perm"
+  top: "conv4_3_norm_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv4_3_norm"
+  bottom: "data"
+  top: "conv4_3_norm_mbox_priorbox"
+  prior_box_param {
+    min_size: 30.0
+    max_size: 60.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 8
+    offset: 0.5
+  }
+}
+layer {
+  name: "fc7_mbox_loc"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "fc7_mbox_loc_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_loc"
+  top: "fc7_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_loc_perm"
+  top: "fc7_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "fc7_mbox_conf_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_conf"
+  top: "fc7_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_conf_perm"
+  top: "fc7_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "fc7"
+  bottom: "data"
+  top: "fc7_mbox_priorbox"
+  prior_box_param {
+    min_size: 60.0
+    max_size: 111.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 16
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv6_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_loc"
+  top: "conv6_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_loc_perm"
+  top: "conv6_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv6_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_conf"
+  top: "conv6_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_conf_perm"
+  top: "conv6_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv6_2_h"
+  bottom: "data"
+  top: "conv6_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 111.0
+    max_size: 162.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 32
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv7_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv7_2_mbox_loc"
+  top: "conv7_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv7_2_mbox_loc_perm"
+  top: "conv7_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv7_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv7_2_mbox_conf"
+  top: "conv7_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv7_2_mbox_conf_perm"
+  top: "conv7_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv7_2_h"
+  bottom: "data"
+  top: "conv7_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 162.0
+    max_size: 213.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 64
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv8_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv8_2_mbox_loc"
+  top: "conv8_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv8_2_mbox_loc_perm"
+  top: "conv8_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv8_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv8_2_mbox_conf"
+  top: "conv8_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv8_2_mbox_conf_perm"
+  top: "conv8_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv8_2_h"
+  bottom: "data"
+  top: "conv8_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 213.0
+    max_size: 264.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 100
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv9_2_h"
+  top: "conv9_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_loc"
+  top: "conv9_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_loc_perm"
+  top: "conv9_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv9_2_h"
+  top: "conv9_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_conf"
+  top: "conv9_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_conf_perm"
+  top: "conv9_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv9_2_h"
+  bottom: "data"
+  top: "conv9_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 264.0
+    max_size: 315.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 300
+    offset: 0.5
+  }
+}
+layer {
+  name: "mbox_loc"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_loc_flat"
+  bottom: "fc7_mbox_loc_flat"
+  bottom: "conv6_2_mbox_loc_flat"
+  bottom: "conv7_2_mbox_loc_flat"
+  bottom: "conv8_2_mbox_loc_flat"
+  bottom: "conv9_2_mbox_loc_flat"
+  top: "mbox_loc"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_conf"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_conf_flat"
+  bottom: "fc7_mbox_conf_flat"
+  bottom: "conv6_2_mbox_conf_flat"
+  bottom: "conv7_2_mbox_conf_flat"
+  bottom: "conv8_2_mbox_conf_flat"
+  bottom: "conv9_2_mbox_conf_flat"
+  top: "mbox_conf"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_priorbox"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_priorbox"
+  bottom: "fc7_mbox_priorbox"
+  bottom: "conv6_2_mbox_priorbox"
+  bottom: "conv7_2_mbox_priorbox"
+  bottom: "conv8_2_mbox_priorbox"
+  bottom: "conv9_2_mbox_priorbox"
+  top: "mbox_priorbox"
+  concat_param {
+    axis: 2
+  }
+}
+
+layer {
+  name: "mbox_conf_reshape"
+  type: "Reshape"
+  bottom: "mbox_conf"
+  top: "mbox_conf_reshape"
+  reshape_param {
+    shape {
+      dim: 0
+      dim: -1
+      dim: 2
+    }
+  }
+}
+layer {
+  name: "mbox_conf_softmax"
+  type: "Softmax"
+  bottom: "mbox_conf_reshape"
+  top: "mbox_conf_softmax"
+  softmax_param {
+    axis: 2
+  }
+}
+layer {
+  name: "mbox_conf_flatten"
+  type: "Flatten"
+  bottom: "mbox_conf_softmax"
+  top: "mbox_conf_flatten"
+  flatten_param {
+    axis: 1
+  }
+}
+
+layer {
+  name: "detection_out"
+  type: "DetectionOutput"
+  bottom: "mbox_loc"
+  bottom: "mbox_conf_flatten"
+  bottom: "mbox_priorbox"
+  top: "detection_out"
+  include {
+    phase: TEST
+  }
+  detection_output_param {
+    num_classes: 2
+    share_location: true
+    background_label_id: 0
+    nms_param {
+      nms_threshold: 0.45
+      top_k: 400
+    }
+    code_type: CENTER_SIZE
+    keep_top_k: 200
+    confidence_threshold: 0.01
+  }
+}

functions.py

@@ -0,0 +1,58 @@
+import cv2
+import numpy as np
+import dlib
+
+dnn_net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel")
+
+# Initialize dlib's facial landmark predictor
+predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+
+dnn_net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel")
+
+# Initialize dlib's facial landmark predictor
+predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+
+def get_face(image, net=dnn_net, predictor=predictor):
+    # Prepare the image for DNN face detection
+    (h, w) = image.shape[:2]
+    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
+    net.setInput(blob)
+    detections = net.forward()
+
+    # Loop over the detections
+    for i in range(0, detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+
+        # Filter out weak detections
+        if confidence > 0.5:
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startX, startY, endX, endY) = box.astype("int")
+
+            # Convert bounding box to dlib rectangle format
+            dlib_rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            landmarks = predictor(gray, dlib_rect)
+
+            # Visualize landmarks
+            for p in landmarks.parts():
+                cv2.circle(image, (p.x, p.y), 2, (0, 255, 0), -1)
+
+            # Get the bounding box for the face based on landmarks
+            landmarks_np = np.array([[p.x, p.y] for p in landmarks.parts()])
+            x, y, w, h = cv2.boundingRect(landmarks_np)
+            x -= 25
+            y -= 25
+            w += 50
+            h += 50
+
+            x = max(0, x)
+            y = max(0, y)
+            w = min(w, image.shape[1] - x)
+            h = min(h, image.shape[0] - y)
+
+            # Crop and resize the face
+            face_crop = image[y-h//2:y+h, x:x+w]
+            face_crop = cv2.resize(face_crop, (224, 224))
+            return face_crop
+
+    return None

incept_v3_10fps_full_dp0.4.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c76de29d4085ca4b5f903e7a24c9afe8e836e0b125cc2ae39e6dc59a214ed800
+size 296199144

res10_300x300_ssd_iter_140000.caffemodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a56a11a57a4a295956b0660b4a3d76bbdca2206c4961cea8efe7d95c7cb2f2d
+size 10666211