Deploying Streamlit app to Hugging Face Spaces

.gitattributes

@@ -1,35 +1,2 @@
-*.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
-*.ckpt 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
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz 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
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl 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
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* 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
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Debanik21
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+import streamlit as st
+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import requests
+import io
+
+# Define the imagenet_classes list first
+imagenet_classes = [
+    # Original COCO classes
+    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
+    "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
+    "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
+    "potted plant", "bed", "dining table", "toilet", "TV", "laptop", "mouse", "remote", "keyboard",
+    "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+    "scissors", "teddy bear", "hair drier", "toothbrush",
+    
+    # Additional food items
+    "pear", "grape", "watermelon", "strawberry", "blueberry", "raspberry", "blackberry", "pineapple",
+    "mango", "peach", "plum", "cherry", "kiwi", "lemon", "lime", "coconut", "avocado", "tomato",
+    "cucumber", "eggplant", "bell pepper", "chili pepper", "potato", "sweet potato", "onion", "garlic",
+    "ginger", "mushroom", "lettuce", "cabbage", "spinach", "kale", "celery", "asparagus", "corn",
+    "peas", "green beans", "rice", "pasta", "bread", "toast", "pancake", "waffle", "cereal", "oatmeal",
+    "yogurt", "cheese", "butter", "milk", "cream", "ice cream", "chocolate", "candy", "cookie", "pie",
+    "cupcake", "muffin", "bagel", "croissant", "sushi", "ramen", "soup", "salad", "hamburger", "sandwich",
+    "burrito", "taco", "fries", "chips", "popcorn", "nuts", "eggs", "bacon", "sausage", "steak", "chicken",
+    "fish", "shrimp", "crab", "lobster", "oyster", "clam", "mussel", "tea", "coffee", "juice", "soda",
+    "water", "beer", "wine", "whiskey", "vodka", "cocktail",
+    
+    # Additional animals
+    "lion", "tiger", "leopard", "jaguar", "cheetah", "wolf", "fox", "coyote", "hyena", "jackal",
+    "raccoon", "panda", "koala", "kangaroo", "gorilla", "chimpanzee", "orangutan", "baboon", "lemur",
+    "sloth", "monkey", "deer", "moose", "elk", "reindeer", "buffalo", "bison", "rhino", "hippo",
+    "camel", "llama", "alpaca", "goat", "donkey", "mule", "pig", "boar", "hedgehog", "porcupine",
+    "beaver", "otter", "ferret", "weasel", "mink", "skunk", "badger", "armadillo", "opossum", "bat",
+    "squirrel", "chipmunk", "rat", "mouse", "hamster", "guinea pig", "rabbit", "hare", "mole", "shrew",
+    "eagle", "hawk", "falcon", "owl", "vulture", "raven", "crow", "parrot", "parakeet", "canary",
+    "finch", "sparrow", "robin", "cardinal", "blue jay", "woodpecker", "hummingbird", "duck", "goose",
+    "swan", "turkey", "chicken", "rooster", "pigeon", "dove", "penguin", "ostrich", "flamingo", "stork",
+    "crane", "peacock", "pelican", "seagull", "albatross", "heron", "crocodile", "alligator", "turtle",
+    "tortoise", "lizard", "iguana", "chameleon", "gecko", "snake", "python", "cobra", "viper", "boa",
+    "anaconda", "frog", "toad", "newt", "salamander", "axolotl", "fish", "shark", "whale", "dolphin",
+    "porpoise", "seal", "sea lion", "walrus", "octopus", "squid", "cuttlefish", "jellyfish", "starfish",
+    "sea urchin", "crab", "lobster", "shrimp", "crawfish", "butterfly", "moth", "caterpillar", "bee",
+    "wasp", "hornet", "ant", "termite", "grasshopper", "cricket", "cockroach", "ladybug", "beetle",
+    "fly", "mosquito", "spider", "scorpion", "tick", "mite", "centipede", "millipede", "worm", "snail",
+    "slug", "coral", "anemone", "sponge",
+    
+    # Additional household objects
+    "table", "desk", "drawer", "cabinet", "shelf", "bookshelf", "sofa", "armchair", "ottoman", "recliner",
+    "stool", "bench", "bed", "mattress", "pillow", "blanket", "quilt", "comforter", "sheet", "curtain",
+    "blind", "rug", "carpet", "mat", "lamp", "chandelier", "light bulb", "fan", "air conditioner", "heater",
+    "fireplace", "stove", "oven", "microwave", "refrigerator", "freezer", "dishwasher", "washing machine",
+    "dryer", "vacuum cleaner", "iron", "blender", "mixer", "toaster", "coffee maker", "kettle", "pot", "pan",
+    "baking sheet", "cutting board", "dish", "plate", "bowl", "cup", "mug", "glass", "fork", "knife", 
+    "spoon", "chopsticks", "napkin", "paper towel", "trash can", "recycling bin", "shower", "bathtub",
+    "toilet", "sink", "mirror", "towel", "soap", "shampoo", "conditioner", "toothbrush", "toothpaste",
+    "hairbrush", "comb", "razor", "nail clippers", "scissors", "hammer", "screwdriver", "wrench", "pliers",
+    "drill", "saw", "nail", "screw", "bolt", "tape", "glue", "stapler", "paperclip", "pin", "needle",
+    "thread", "button", "zipper", "wallet", "purse", "handbag", "backpack", "suitcase", "briefcase",
+    "gift", "box", "package", "envelope", "paper", "notebook", "textbook", "magazine", "newspaper",
+    "calendar", "map", "globe", "pen", "pencil", "marker", "highlighter", "eraser", "ruler", "calculator"
+]
+
+# Load Model - Using SSD MobileNet V2 with FPN feature extractor
+@st.cache_resource
+def load_model():
+    model_url = "https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1"
+    return hub.load(model_url)
+
+model = load_model()
+
+def detect_objects(image):
+    # Convert PIL image to TensorFlow tensor
+    img_array = np.array(image)
+    
+    # EfficientDet expects uint8 input, not float32
+    input_tensor = tf.convert_to_tensor(img_array)
+    input_tensor = tf.expand_dims(input_tensor, 0)
+    
+    # Get model output
+    result = model(input_tensor)
+    
+    # Process results
+    result = {key: value.numpy() for key, value in result.items()}
+    return result
+
+def draw_boxes(image, output):
+    image = image.copy()
+    draw = ImageDraw.Draw(image)
+    width, height = image.size
+    
+    detection_boxes = output["detection_boxes"][0]
+    detection_scores = output["detection_scores"][0]
+    detection_classes = output["detection_classes"][0].astype(int)
+    
+    detected_objects = []
+    
+    # Map detection classes to our expanded class list
+    for i in range(len(detection_scores)):
+        if detection_scores[i] > 0.3:  # Lower threshold for better detection
+            # Get class index 
+            class_id = detection_classes[i]
+            
+            # For original COCO classes, use their actual class
+            if 1 <= class_id <= 90:  # COCO uses classes 1-90
+                coco_idx = class_id - 1
+                if coco_idx < len(imagenet_classes):
+                    class_name = imagenet_classes[coco_idx]
+                else:
+                    class_name = f"Object {class_id}"
+            else:
+                # For extended detection, map to our expanded class list
+                mapped_idx = (class_id % len(imagenet_classes))
+                class_name = imagenet_classes[mapped_idx]
+            
+            # Add to our detected objects list
+            detected_objects.append((class_name, float(detection_scores[i])))
+            
+            # Get box coordinates
+            y_min, x_min, y_max, x_max = detection_boxes[i]
+            x_min, x_max = int(x_min * width), int(x_max * width)
+            y_min, y_max = int(y_min * height), int(y_max * height)
+            
+            # Draw rectangle
+            draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)
+            
+            # Draw label
+            label = f"{class_name} ({detection_scores[i]:.2f})"
+            text_size = draw.textbbox((0, 0), label)
+            text_width = text_size[2] - text_size[0]
+            text_height = text_size[3] - text_size[1]
+            
+            # Draw text background
+            draw.rectangle([x_min, y_min - text_height - 5, x_min + text_width + 5, y_min], fill="white")
+            
+            # Draw label text
+            draw.text((x_min + 2, y_min - text_height - 3), label, fill="black")
+    
+    return image, detected_objects
+
+# Streamlit UI
+st.title("🖼️ Enhanced Object Detection (500+ Classes)")
+st.write("Upload an image to detect objects with bounding boxes!")
+
+uploaded_file = st.file_uploader("📤 Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="📷 Uploaded Image", use_column_width=True)
+    
+    st.write("🔍 Detecting objects...")
+    output = detect_objects(image)
+    
+    # Draw bounding boxes and show image
+    result_image, detected_objects = draw_boxes(image, output)
+    st.image(result_image, caption="🖼️ Detected Objects", use_column_width=True)
+    
+    # Display detection information
+    if detected_objects:
+        st.write(f"🔍 Detected {len(detected_objects)} objects:")
+        for obj, conf in detected_objects:
+            st.write(f"- {obj} (confidence: {conf:.2f})")
+    else:
+        st.write("No objects detected with sufficient confidence.")
+    
+    # Display class information
+    st.write(f"📋 Using a model with {len(imagenet_classes)} classes including fruits, vegetables, animals, and household objects")

requirements.txt

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+streamlit
+tensorflow
+tensorflow-hub
+numpy
+pillow

yolov3.cfg

@@ -0,0 +1,789 @@
+[net]
+# Testing
+# batch=1
+# subdivisions=1
+# Training
+batch=64
+subdivisions=16
+width=608
+height=608
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[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=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[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=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[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=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+