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.gitattributes

@@ -32,3 +32,7 @@ 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
+eported_models/efficientdet_d0_ocr_numberplate/checkpoint/ckpt-0.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+eported_models/efficientdet_d0_ocr_numberplate/saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+eported_models/ssd_mobilnet_numberplate_region_detection/checkpoint/ckpt-0.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+eported_models/ssd_mobilnet_numberplate_region_detection/saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+from streamlit_image_select import image_select
+import tensorflow as tf
+import datetime
+import os
+import pandas as pd
+import numpy as np
+import cv2 
+from PIL import Image
+
+
+# trained model files and paths
+files = {
+    "label_map_ssd" : os.path.join('eported_models','ssd_mobilnet_numberplate_region_detection','label_map.pbtxt'),
+    "label_map_efficientdet" : os.path.join('eported_models','efficientdet_d0_ocr_numberplate','label_map.pbtxt')
+}
+
+paths = {
+    "saved_model_path_ssd" : os.path.join('eported_models','ssd_mobilnet_numberplate_region_detection','saved_model'),
+    "saved_model_path_efficientdet" : os.path.join('eported_models','efficientdet_d0_ocr_numberplate','saved_model')
+}
+
+def read_label_map(label_map_path):
+    item_id = None
+    item_name = None
+    items = {}
+    with open(label_map_path, "r") as file:
+        for line in file:
+            line.replace(" ", "")
+            if line == "item{":
+                pass
+            elif line == "}":
+                pass
+            elif "id" in line:
+                item_id = int(line.split(":", 1)[1].strip())
+            elif "name" in line:
+                item_name = {line.split(":")[0].replace("\"", " ").strip() : line.split(":")[1].replace("'", '').strip()}
+            if item_id is not None and item_name is not None:
+                items[item_id] = item_name
+                item_id = None
+                item_name = None
+    return items
+
+#load model
+@st.cache(allow_output_mutation = True)
+def cache_model(path1, path2):
+    model1 = tf.saved_model.load(path1)
+    model2 = tf.saved_model.load(path2)
+    return (model1, model2)
+detect_fn_ssd, detect_fn_efficientdet = cache_model(paths["saved_model_path_ssd"], paths["saved_model_path_efficientdet"])
+
+# Creating category index
+category_index_ssd = read_label_map(files["label_map_ssd"])
+category_index_efficientdet = read_label_map(files["label_map_efficientdet"])
+
+def image_resize_with_padding(image):
+    image = np.array(image)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    old_size = image.shape[:2] # old_size is in (height, width) format
+    
+    if max(old_size) > 256:
+        desired_size = 512
+    else:
+        desired_size = 256
+        
+    ratio = float(desired_size)/max(old_size)
+    new_size = tuple([int(x*ratio) for x in old_size])
+    # resize the image
+    resized = cv2.resize(image, (new_size[1],new_size[0]))
+
+    delta_w = desired_size - new_size[1]
+    delta_h = desired_size - new_size[0]
+    top, bottom = delta_h//2, delta_h-(delta_h//2)
+    left, right = delta_w//2, delta_w-(delta_w//2)
+    # print(top,bottom,left,right)
+
+    color = [255, 255, 255]
+    new_im = cv2.copyMakeBorder(resized, top, bottom, left, right, cv2.BORDER_CONSTANT,value=color)
+    
+    return new_im
+
+def ExtractBBoxes(bboxes, bclasses, bscores, im_width, im_height, threshold, category_index):
+    bbox = []
+    class_labels = []
+    for idx in range(len(bboxes)):
+        if bscores[idx] >= threshold:
+          y_min = int(bboxes[idx][0] * im_height)
+          x_min = int(bboxes[idx][1] * im_width)
+          y_max = int(bboxes[idx][2] * im_height)
+          x_max = int(bboxes[idx][3] * im_width)
+          class_label = category_index[int(bclasses[idx])]['name']
+          class_labels.append(class_label)
+          bbox.append([x_min, y_min, x_max, y_max, class_label, float(bscores[idx])])
+    return (bbox, class_labels)
+
+def ocr_predict(img,threshold):
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    image_np = tf.convert_to_tensor(img, dtype=tf.uint8)
+    input_tensor = np.expand_dims(image_np, 0)
+    image_height, image_width, _ = image_np.shape
+    detections = detect_fn_efficientdet(input_tensor)
+    bboxes = detections['detection_boxes'][0].numpy()
+    bclasses = detections['detection_classes'][0].numpy().astype(np.int32)
+    bscores = detections['detection_scores'][0].numpy()
+    det_boxes, class_labels = ExtractBBoxes(bboxes, bclasses, bscores, image_width, image_height, threshold, category_index_efficientdet)
+    output = []
+    for detection in det_boxes:
+        x_min, y_min, x_max, y_max, label, score = detection[0], detection[1], detection[2], detection[3], detection[4], round(detection[5])
+        
+        output.append((label, int(x_min*image_width), int(y_min*image_height),
+                      int(x_max*image_width), int(y_max*image_height), score))
+
+    df = pd.DataFrame(output, columns = ['label','xmin','ymin','xmax','ymax', 'score'])
+   
+    df_up = df[df.ymin < (df.ymin.min()*1.2)].sort_values(by  = ['xmin'])
+    df_down = df[df.ymin > (df.ymin.min()*1.2)].sort_values(by  = ['xmin'])
+    df = pd.concat([df_up,df_down])
+    vehicle_number = "".join(df["label"])
+    
+    
+    
+    current_date_time = datetime.datetime.now()
+    day = current_date_time.strftime("%A")
+    date = current_date_time.strftime("%d/%m/%Y")
+    time = current_date_time.strftime("%I:%M:%S %p")
+    data = [(vehicle_number,day,date,time)]
+
+    return (data)
+
+def predict(img,threshold):
+    image_np = np.array(img)
+    img = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+    image = tf.convert_to_tensor(img, dtype=tf.uint8)
+    input_tensor = np.expand_dims(image, 0)
+    # image = tf.image.decode_image(open(file, 'rb').read(), channels=3)
+    # img = cv2.imread(file)
+    image_height, image_width, _ = image.shape
+    
+    
+    
+    # input_tensor = np.expand_dims(image, 0)
+    detections = detect_fn_ssd(input_tensor)
+
+    bboxes = detections['detection_boxes'][0].numpy()
+    bclasses = detections['detection_classes'][0].numpy().astype(np.int32)
+    bscores = detections['detection_scores'][0].numpy()
+    det_boxes, class_labels = ExtractBBoxes(bboxes, bclasses, bscores, image_width, image_height, threshold, category_index_ssd)
+    output = []
+    for detection in det_boxes:
+        x_min, y_min, x_max, y_max, label, score = detection[0], detection[1], detection[2], detection[3], detection[4], round(detection[5])
+        
+        output.append((label, x_min, y_min, x_max, y_max, score))
+        
+    image_np_with_detections = image_np.copy()
+
+    data_list = []
+    for l, x_min, y_min, x_max, y_max, score in output:
+
+        array = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+        image = Image.fromarray(array)
+
+        cropped_img = image.crop((x_min, y_min, x_max, y_max))
+        input_ocr_image = image_resize_with_padding(cropped_img)
+
+        data = ocr_predict(input_ocr_image,threshold)
+        data_list.append(data)
+
+    for l, x_min, y_min, x_max, y_max, score in output:
+        
+
+        x1 = x_min
+        y1 = y_min
+        x2 = x_max
+        y2 = y_max
+        # For bounding box
+        color = (0,255,0)
+
+        img = cv2.rectangle(img, (x1, y1), (x2, y2),color, 2)
+
+        label = f"{l} : {round(score,2)}"
+        text_color = (0,0,255)
+        # For the text background
+        # Finds space required by the text so that we can put a background with that amount of width.
+        (w, h), _ = cv2.getTextSize(
+                label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
+
+        # Prints the text.    
+        img = cv2.rectangle(img, (x1, y1 - 20), (x1 + w, y1), color, -1)
+        img = cv2.putText(img, label, (x1, y1 - 5),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, text_color, 1)
+        # plt.imshow(img)
+
+
+    im_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    return (im_rgb, data_list)
+
+st.write("""
+         # Automated Number Plate Recognition
+         """
+         )
+
+st.write("""
+        **For full code implementation and sub modules please visit the [Github link](https://github.com/gourav300/Automated-Number-plate-Recognition-for-Indian-Vehicles)**
+        """)
+
+### load file
+uploaded_file = st.file_uploader("Upload an image file for a vehicle with standard number plate", type=["jpg", "png", "jpeg"])
+
+st.write("Note -: if you have uploaded an image please click X to enable below images")
+test_img = image_select(
+    label='''Select an image to get number plate data''',
+    images=[
+        "test_images/test3.png",
+        "test_images/test2.png",
+        "test_images/test1.png",
+        "test_images/test4.jpg",
+        "test_images/test5.jpg",
+        "test_images/test6.jpg",
+        "test_images/test7.jpg",
+        "test_images/test8.jpg",
+    ])
+
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    img, data = predict(image, 0.6)
+    # image = Image.open(uploaded_file)
+    st.image(img,width = 500)#, use_column_width=True)
+    st.write(f'''
+    Vehicle details : 
+
+    {data}
+
+    ''')
+else:
+
+    image = Image.open(test_img)
+    img, data = predict(image, 0.6)
+    # image = Image.open(uploaded_file)
+    st.image(img,width = 500)#, use_column_width=True)
+    st.write(f'''
+    Vehicle details : 
+
+    {data}
+
+    ''')

eported_models/efficientdet_d0_ocr_numberplate/.ipynb_checkpoints/label_map-checkpoint.pbtxt

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eported_models/efficientdet_d0_ocr_numberplate/checkpoint/checkpoint

@@ -0,0 +1,4 @@
+model_checkpoint_path: "ckpt-0"
+all_model_checkpoint_paths: "ckpt-0"
+all_model_checkpoint_timestamps: 1656053241.2777963
+last_preserved_timestamp: 1656053239.3308609

eported_models/efficientdet_d0_ocr_numberplate/checkpoint/ckpt-0.data-00000-of-00001

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eported_models/efficientdet_d0_ocr_numberplate/label_map.pbtxt

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eported_models/efficientdet_d0_ocr_numberplate/pipeline.config

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+model {
+  ssd {
+    num_classes: 36
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 512
+        max_dimension: 512
+        pad_to_max_dimension: true
+      }
+    }
+    feature_extractor {
+      type: "ssd_efficientnet-b0_bifpn_keras"
+      conv_hyperparams {
+        regularizer {
+          l2_regularizer {
+            weight: 4e-05
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            mean: 0.0
+            stddev: 0.03
+          }
+        }
+        activation: SWISH
+        batch_norm {
+          decay: 0.99
+          scale: true
+          epsilon: 0.001
+        }
+        force_use_bias: true
+      }
+      bifpn {
+        min_level: 3
+        max_level: 7
+        num_iterations: 3
+        num_filters: 64
+      }
+    }
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 1.0
+        x_scale: 1.0
+        height_scale: 1.0
+        width_scale: 1.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+              weight: 4e-05
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              mean: 0.0
+              stddev: 0.01
+            }
+          }
+          activation: SWISH
+          batch_norm {
+            decay: 0.99
+            scale: true
+            epsilon: 0.001
+          }
+          force_use_bias: true
+        }
+        depth: 64
+        num_layers_before_predictor: 3
+        kernel_size: 3
+        class_prediction_bias_init: -4.6
+        use_depthwise: true
+      }
+    }
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        scales_per_octave: 3
+      }
+    }
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-08
+        iou_threshold: 0.5
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+    normalize_loss_by_num_matches: true
+    loss {
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_sigmoid_focal {
+          gamma: 1.5
+          alpha: 0.25
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    encode_background_as_zeros: true
+    normalize_loc_loss_by_codesize: true
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    add_background_class: false
+  }
+}
+train_config {
+  batch_size: 4
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_scale_crop_and_pad_to_square {
+      output_size: 512
+      scale_min: 0.1
+      scale_max: 2.0
+    }
+  }
+  sync_replicas: true
+  optimizer {
+    momentum_optimizer {
+      learning_rate {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.08
+          total_steps: 300000
+          warmup_learning_rate: 0.001
+          warmup_steps: 2500
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  fine_tune_checkpoint: "Tensorflow/workspace/pre-trained-models/efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"
+  num_steps: 300000
+  startup_delay_steps: 0.0
+  replicas_to_aggregate: 8
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+  fine_tune_checkpoint_type: "detection"
+  use_bfloat16: true
+  fine_tune_checkpoint_version: V2
+}
+train_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/train.record"
+  }
+}
+eval_config {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  batch_size: 1
+}
+eval_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  shuffle: false
+  num_epochs: 1
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/validation.record"
+  }
+}

eported_models/efficientdet_d0_ocr_numberplate/saved_model/saved_model.pb

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eported_models/efficientdet_d0_ocr_numberplate/saved_model/variables/variables.data-00000-of-00001

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eported_models/ssd_mobilnet_numberplate_region_detection/.ipynb_checkpoints/label_map-checkpoint.pbtxt

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+	name:'np'
+	id:1
+}

eported_models/ssd_mobilnet_numberplate_region_detection/.ipynb_checkpoints/pipeline-checkpoint.config

@@ -0,0 +1,191 @@
+model {
+  ssd {
+    num_classes: 1
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    feature_extractor {
+      type: "ssd_mobilenet_v2_fpn_keras"
+      depth_multiplier: 1.0
+      min_depth: 16
+      conv_hyperparams {
+        regularizer {
+          l2_regularizer {
+            weight: 4e-05
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            mean: 0.0
+            stddev: 0.01
+          }
+        }
+        activation: RELU_6
+        batch_norm {
+          decay: 0.997
+          scale: true
+          epsilon: 0.001
+        }
+      }
+      use_depthwise: true
+      override_base_feature_extractor_hyperparams: true
+      fpn {
+        min_level: 3
+        max_level: 7
+        additional_layer_depth: 128
+      }
+    }
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+              weight: 4e-05
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              mean: 0.0
+              stddev: 0.01
+            }
+          }
+          activation: RELU_6
+          batch_norm {
+            decay: 0.997
+            scale: true
+            epsilon: 0.001
+          }
+        }
+        depth: 128
+        num_layers_before_predictor: 4
+        kernel_size: 3
+        class_prediction_bias_init: -4.6
+        share_prediction_tower: true
+        use_depthwise: true
+      }
+    }
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        scales_per_octave: 2
+      }
+    }
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-08
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+        use_static_shapes: false
+      }
+      score_converter: SIGMOID
+    }
+    normalize_loss_by_num_matches: true
+    loss {
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_sigmoid_focal {
+          gamma: 2.0
+          alpha: 0.25
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    encode_background_as_zeros: true
+    normalize_loc_loss_by_codesize: true
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+  }
+}
+train_config {
+  batch_size: 4
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  sync_replicas: true
+  optimizer {
+    momentum_optimizer {
+      learning_rate {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.08
+          total_steps: 50000
+          warmup_learning_rate: 0.026666
+          warmup_steps: 1000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  fine_tune_checkpoint: "Tensorflow/workspace/pre-trained-models/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0"
+  num_steps: 50000
+  startup_delay_steps: 0.0
+  replicas_to_aggregate: 8
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+  fine_tune_checkpoint_type: "detection"
+  fine_tune_checkpoint_version: V2
+}
+train_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/train.record"
+  }
+}
+eval_config {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+}
+eval_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  shuffle: false
+  num_epochs: 1
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/test.record"
+  }
+}

eported_models/ssd_mobilnet_numberplate_region_detection/checkpoint/checkpoint

@@ -0,0 +1,4 @@
+model_checkpoint_path: "ckpt-0"
+all_model_checkpoint_paths: "ckpt-0"
+all_model_checkpoint_timestamps: 1656053463.9657378
+last_preserved_timestamp: 1656053462.6085548

eported_models/ssd_mobilnet_numberplate_region_detection/checkpoint/ckpt-0.data-00000-of-00001

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eported_models/ssd_mobilnet_numberplate_region_detection/label_map.pbtxt

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+item { 
+	name:'np'
+	id:1
+}

eported_models/ssd_mobilnet_numberplate_region_detection/pipeline.config

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+model {
+  ssd {
+    num_classes: 1
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    feature_extractor {
+      type: "ssd_mobilenet_v2_fpn_keras"
+      depth_multiplier: 1.0
+      min_depth: 16
+      conv_hyperparams {
+        regularizer {
+          l2_regularizer {
+            weight: 4e-05
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            mean: 0.0
+            stddev: 0.01
+          }
+        }
+        activation: RELU_6
+        batch_norm {
+          decay: 0.997
+          scale: true
+          epsilon: 0.001
+        }
+      }
+      use_depthwise: true
+      override_base_feature_extractor_hyperparams: true
+      fpn {
+        min_level: 3
+        max_level: 7
+        additional_layer_depth: 128
+      }
+    }
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+              weight: 4e-05
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              mean: 0.0
+              stddev: 0.01
+            }
+          }
+          activation: RELU_6
+          batch_norm {
+            decay: 0.997
+            scale: true
+            epsilon: 0.001
+          }
+        }
+        depth: 128
+        num_layers_before_predictor: 4
+        kernel_size: 3
+        class_prediction_bias_init: -4.6
+        share_prediction_tower: true
+        use_depthwise: true
+      }
+    }
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        scales_per_octave: 2
+      }
+    }
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-08
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+        use_static_shapes: false
+      }
+      score_converter: SIGMOID
+    }
+    normalize_loss_by_num_matches: true
+    loss {
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_sigmoid_focal {
+          gamma: 2.0
+          alpha: 0.25
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    encode_background_as_zeros: true
+    normalize_loc_loss_by_codesize: true
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+  }
+}
+train_config {
+  batch_size: 4
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  sync_replicas: true
+  optimizer {
+    momentum_optimizer {
+      learning_rate {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.08
+          total_steps: 50000
+          warmup_learning_rate: 0.026666
+          warmup_steps: 1000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  fine_tune_checkpoint: "Tensorflow/workspace/pre-trained-models/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0"
+  num_steps: 50000
+  startup_delay_steps: 0.0
+  replicas_to_aggregate: 8
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+  fine_tune_checkpoint_type: "detection"
+  fine_tune_checkpoint_version: V2
+}
+train_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/train.record"
+  }
+}
+eval_config {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+}
+eval_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  shuffle: false
+  num_epochs: 1
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/test.record"
+  }
+}

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requirements.txt

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+tensorflow == 2.11.0
+streamlit == 1.15.2
+Pillow == 9.3.0
+streamlit-image-select == 0.5.1
+pandas == 1.5.2
+numpy == 1.23.4
+opencv-python == 4.6.0.66

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