Duplicate from masapasa/damage-detections

.gitattributes

@@ -0,0 +1,28 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow 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
+*.wasm 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
+yolov4-custom_best.weights filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,14 @@
+---
+title: Oral_Cancer_Detection
+emoji: 👀
+colorFrom: indigo
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.9.0
+app_file: app.py
+pinned: false
+license: afl-3.0
+duplicated_from: masapasa/damage-detections
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -0,0 +1,209 @@
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow import keras
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import Dropout
+from keras.layers import Flatten
+from keras.constraints import maxnorm
+from tensorflow.keras.optimizers import SGD
+from keras.layers.convolutional import Conv2D
+from keras.layers import Dense, Conv2D ,Flatten,Dropout,MaxPool2D, BatchNormalization
+from keras.utils import np_utils
+import tensorflow as tf
+from keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.preprocessing import image_dataset_from_directory  
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.vgg19 import VGG19
+import keras
+from PIL import Image
+import matplotlib.pyplot as plt
+import seaborn
+from sklearn.metrics import confusion_matrix , classification_report
+import os
+import cv2
+from skimage.transform import resize
+import streamlit as st
+
+def get_output_layers(net):
+    
+    layer_names = net.getLayerNames()
+    
+    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+
+    return output_layers
+
+# function to draw bounding box on the detected object with class name
+def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h, COLORS):
+
+    label = f'damage:{confidence}'
+
+    color = COLORS[class_id]
+
+    cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
+
+    cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+    # plt.imshow(img)
+    # plt.show()
+
+def detection_inference(image, scale = 1/255, image_size = 416, conf_threshold = 0.1, nms_threshold = 0.4):
+  Width = image.shape[1]
+  Height = image.shape[0]
+
+  net=cv2.dnn.readNet('yolov4-custom_best.weights','yolov4-custom.cfg')
+  COLORS = np.random.uniform(0, 255, size=(1, 3))
+
+  blob = cv2.dnn.blobFromImage(image, scale, (image_size, image_size), (0,0,0), True, crop=False)
+  net.setInput(blob)
+
+  outs = net.forward(get_output_layers(net))
+
+  class_ids = []
+  confidences = []
+  boxes = []
+
+  for out in outs:
+    for detection in out:
+      scores=detection[5:]
+      class_id=np.argmax(scores)
+      confidence=scores[class_id]
+      if confidence > 0.1:
+        center_x = int(detection[0] * Width)
+        center_y = int(detection[1] * Height)
+        w = int(detection[2] * Width)
+        h = int(detection[3] * Height)
+        x = center_x - w / 2
+        y = center_y - h / 2
+        class_ids.append(class_id)
+        confidences.append(float(confidence))
+        boxes.append([x, y, w, h])
+
+  indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
+
+  for i in indices:
+    i = i[0]
+    box = boxes[i]
+    x = box[0]
+    y = box[1]
+    w = box[2]
+    h = box[3]
+
+    draw_bounding_box(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h), COLORS)
+
+  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+  plt.imshow(image)
+  plt.show()
+  
+  return image
+  # st.image(image, caption='Object detection output', use_column_width=True)
+
+def _predict(img, model):
+  m = keras.models.load_model(model)
+  img2 = img.resize((224, 224))
+
+  image_array = np.asarray(img2)
+  new_one = image_array.reshape((1, 224, 224, 3))
+
+  y_pred = m(new_one)
+  print(y_pred)
+  val = np.argmax(y_pred, axis = 1)
+  return y_pred, val
+  
+@tf.custom_gradient
+def guidedRelu(x):
+  def grad(dy):
+    return tf.cast(dy>0,"float32") * tf.cast(x>0, "float32") * dy
+  return tf.nn.relu(x), grad
+
+def gradcam(img, model):
+  m = keras.models.load_model(model)
+  LAYER_NAME = 'block5_conv4'
+  gb_model = tf.keras.models.Model(
+    inputs = [m.inputs],    
+    outputs = [m.get_layer(LAYER_NAME).output]
+  )
+  layer_dict = [layer for layer in gb_model.layers[1:] if hasattr(layer,'activation')]
+
+  for layer in layer_dict:
+    if layer.activation == tf.keras.activations.relu:
+      layer.activation = guidedRelu
+
+  img2 = img.resize((224, 224))
+
+  image_array = np.asarray(img2)
+  print(image_array.shape)
+  new_one = image_array.reshape((1, 224, 224, 3))
+
+  with tf.GradientTape() as tape:
+    inputs = tf.cast(new_one, tf.float32)
+    tape.watch(inputs)
+    outputs = gb_model(inputs)[0]
+  grads = tape.gradient(outputs,inputs)[0]
+
+  weights = tf.reduce_mean(grads, axis=(0, 1))
+  grad_cam = np.ones(outputs.shape[0: 2], dtype = np.float32)
+  for i, w in enumerate(weights):
+      grad_cam += w * outputs[:, :, i]
+
+  grad_cam_img = cv2.resize(grad_cam.numpy(), (img.size[0], img.size[1]))
+  grad_cam_img = np.maximum(grad_cam_img, 0)
+  heatmap = (grad_cam_img - grad_cam_img.min()) / (grad_cam_img.max() - grad_cam_img.min())
+  grad_cam_img = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
+  output_image = cv2.addWeighted(np.asarray(img).astype('uint8'), 1, grad_cam_img, 0.4, 0)
+  
+  output_img = Image.fromarray(output_image)
+  
+  st.image(output_img, caption='Class Activation Visualization', use_column_width=True)
+
+  plt.imshow(output_image)
+  plt.axis("off")
+  plt.show()
+
+  # guided_back_prop = grads
+  # guided_cam = np.maximum(grad_cam, 0)
+  # guided_cam = guided_cam / np.max(guided_cam) # scale 0 to 1.0
+  # guided_cam = resize(guided_cam, (224,224), preserve_range=True)
+
+  # #pointwise multiplcation of guided backprop and grad CAM 
+  # gd_gb = np.dstack((
+  #         guided_back_prop[:, :, 0] * guided_cam,
+  #         guided_back_prop[:, :, 1] * guided_cam,
+  #         guided_back_prop[:, :, 2] * guided_cam,
+  #     ))
+  # plt.imshow(gd_gb)
+  # plt.axis("off")
+  # plt.show()
+
+uploaded_file = st.file_uploader(
+    "Choose an image of your infrastructure", type=['jpg', 'jpeg', 'png'])
+
+if uploaded_file is not None:
+    img = Image.open(uploaded_file).convert('RGB')
+    cv_img = np.array(img)
+    cv_img = cv2.cvtColor(cv_img, cv2.COLOR_RGB2BGR)
+    # img2 = Image.open('test.jpg')
+    st.image(img, caption='Uploaded file of your infrastructure', use_column_width=True)
+
+    # similarity = ssim(img, img2)
+    # st.write("")
+    # st.write(f'This is {similarity * 100}% histopathological image')
+
+    # if similarity >= 0.85:
+    st.write("")
+    st.write("Classifying...")
+
+    y_pred, val = _predict(img, 'damage-detections.h5')
+    if val == 0:
+      st.write(f'The infrastructure has damage.')
+      final_img = detection_inference(cv_img)
+      final_pil_image = Image.fromarray(final_img)
+      gradcam(final_pil_image, 'damage-detections.h5')
+    else:
+      st.write(f'The infrastructure does not have damage.')
+      gradcam(img, 'damage-detections.h5')
+      
+    
+

damage-detection.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7da652d3775f743f065e4d0289f9ab03f1d7d38b55058150e90f6e7e1044f060
+size 134

damage-detections.h5

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+oid sha256:83636d8b5cd109904b8dddf9c0a8b2cbc5953951dba6272f1301b8cb6c1998ea
+size 558474384

requirements.txt

@@ -0,0 +1,11 @@
+keras==2.8.0
+matplotlib==3.2.2
+numpy==1.21.6
+opencv-contrib-python==3.4.13.47
+pandas==1.3.5
+Pillow==9.1.1
+scikit_image==0.18.3
+scikit_learn==1.1.1
+seaborn==0.11.2
+streamlit==1.9.2
+tensorflow==2.8.0

yolov4-custom.cfg

@@ -0,0 +1,1160 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=1
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 6000
+policy=steps
+steps=4800,5400
+scales=.1,.1
+
+#cutmix=1
+mosaic=1
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+stopbackward=800
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+

yolov4-custom_best.weights

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eedc9817644125a630fcd0cc2a9b0dc3d009cce46a9178c16afbf1d73b96c632
+size 256015980