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Szymon Parafiński
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## keras-yolo3 with Roboflow
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[](LICENSE)
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A Keras implementation of YOLOv3 (Tensorflow backend) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K).
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## What You Will Learn
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* How to load your custom image detection data from Roboflow
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* How set up the YOLOv3 model in keras
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* How to train the YOLOv3 model
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* How to use the model for inference
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* How to save the keras model weights for future use
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## Resources
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* [This blog post](https://blog.roboflow.ai/training-a-yolov3-object-detection-model-with-a-custom-dataset/) provides a deep dive into the tutorial
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* This notebook provides the code necessary to run the tutorial [](https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa)
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* For reading purposes, the notebook is also saved in Tutorial.ipynb
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## About Roboflow for Data Management
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[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.
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Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.
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"""
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Retrain the YOLO model for your own dataset.
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"""
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import os
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import numpy as np
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import keras.backend as K
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from keras.layers import Input, Lambda
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from keras.models import Model
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from keras.optimizers import Adam
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from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
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from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
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from yolo3.utils import get_random_data
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def _main():
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annotation_path = 'train.txt'
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log_dir = 'logs/000/'
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classes_path = 'model_data/coco_classes.txt'
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anchors_path = 'model_data/yolo_anchors.txt'
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class_names = get_classes(classes_path)
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num_classes = len(class_names)
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anchors = get_anchors(anchors_path)
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input_shape = (416,416) # multiple of 32, hw
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model, bottleneck_model, last_layer_model = create_model(input_shape, anchors, num_classes,
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freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze
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logging = TensorBoard(log_dir=log_dir)
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checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
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monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
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early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
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val_split = 0.1
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with open(annotation_path) as f:
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lines = f.readlines()
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np.random.seed(10101)
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np.random.shuffle(lines)
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np.random.seed(None)
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num_val = int(len(lines)*val_split)
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num_train = len(lines) - num_val
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# Train with frozen layers first, to get a stable loss.
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# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
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if True:
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# perform bottleneck training
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if not os.path.isfile("bottlenecks.npz"):
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print("calculating bottlenecks")
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batch_size=8
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bottlenecks=bottleneck_model.predict_generator(data_generator_wrapper(lines, batch_size, input_shape, anchors, num_classes, random=False, verbose=True),
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steps=(len(lines)//batch_size)+1, max_queue_size=1)
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np.savez("bottlenecks.npz", bot0=bottlenecks[0], bot1=bottlenecks[1], bot2=bottlenecks[2])
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# load bottleneck features from file
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dict_bot=np.load("bottlenecks.npz")
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bottlenecks_train=[dict_bot["bot0"][:num_train], dict_bot["bot1"][:num_train], dict_bot["bot2"][:num_train]]
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bottlenecks_val=[dict_bot["bot0"][num_train:], dict_bot["bot1"][num_train:], dict_bot["bot2"][num_train:]]
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# train last layers with fixed bottleneck features
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batch_size=8
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print("Training last layers with bottleneck features")
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print('with {} samples, val on {} samples and batch size {}.'.format(num_train, num_val, batch_size))
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last_layer_model.compile(optimizer='adam', loss={'yolo_loss': lambda y_true, y_pred: y_pred})
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last_layer_model.fit_generator(bottleneck_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, bottlenecks_train),
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steps_per_epoch=max(1, num_train//batch_size),
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validation_data=bottleneck_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, bottlenecks_val),
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validation_steps=max(1, num_val//batch_size),
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epochs=30,
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initial_epoch=0, max_queue_size=1)
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model.save_weights(log_dir + 'trained_weights_stage_0.h5')
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# train last layers with random augmented data
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model.compile(optimizer=Adam(lr=1e-3), loss={
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# use custom yolo_loss Lambda layer.
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'yolo_loss': lambda y_true, y_pred: y_pred})
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batch_size = 16
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print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
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model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
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steps_per_epoch=max(1, num_train//batch_size),
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validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
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validation_steps=max(1, num_val//batch_size),
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epochs=50,
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initial_epoch=0,
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callbacks=[logging, checkpoint])
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model.save_weights(log_dir + 'trained_weights_stage_1.h5')
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# Unfreeze and continue training, to fine-tune.
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# Train longer if the result is not good.
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if True:
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for i in range(len(model.layers)):
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model.layers[i].trainable = True
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model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
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print('Unfreeze all of the layers.')
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batch_size = 4 # note that more GPU memory is required after unfreezing the body
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print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
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model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
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steps_per_epoch=max(1, num_train//batch_size),
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validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
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validation_steps=max(1, num_val//batch_size),
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epochs=100,
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initial_epoch=50,
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callbacks=[logging, checkpoint, reduce_lr, early_stopping])
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model.save_weights(log_dir + 'trained_weights_final.h5')
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# Further training if needed.
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def get_classes(classes_path):
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'''loads the classes'''
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with open(classes_path) as f:
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class_names = f.readlines()
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class_names = [c.strip() for c in class_names]
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return class_names
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def get_anchors(anchors_path):
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'''loads the anchors from a file'''
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with open(anchors_path) as f:
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anchors = f.readline()
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anchors = [float(x) for x in anchors.split(',')]
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return np.array(anchors).reshape(-1, 2)
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def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
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weights_path='model_data/yolo_weights.h5'):
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'''create the training model'''
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K.clear_session() # get a new session
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image_input = Input(shape=(None, None, 3))
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h, w = input_shape
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num_anchors = len(anchors)
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y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
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num_anchors//3, num_classes+5)) for l in range(3)]
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model_body = yolo_body(image_input, num_anchors//3, num_classes)
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print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
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if load_pretrained:
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model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
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print('Load weights {}.'.format(weights_path))
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if freeze_body in [1, 2]:
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# Freeze darknet53 body or freeze all but 3 output layers.
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num = (185, len(model_body.layers)-3)[freeze_body-1]
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for i in range(num): model_body.layers[i].trainable = False
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print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
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# get output of second last layers and create bottleneck model of it
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out1=model_body.layers[246].output
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out2=model_body.layers[247].output
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out3=model_body.layers[248].output
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bottleneck_model = Model([model_body.input, *y_true], [out1, out2, out3])
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# create last layer model of last layers from yolo model
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in0 = Input(shape=bottleneck_model.output[0].shape[1:].as_list())
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in1 = Input(shape=bottleneck_model.output[1].shape[1:].as_list())
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in2 = Input(shape=bottleneck_model.output[2].shape[1:].as_list())
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last_out0=model_body.layers[249](in0)
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last_out1=model_body.layers[250](in1)
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last_out2=model_body.layers[251](in2)
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model_last=Model(inputs=[in0, in1, in2], outputs=[last_out0, last_out1, last_out2])
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model_loss_last =Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
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arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
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[*model_last.output, *y_true])
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last_layer_model = Model([in0,in1,in2, *y_true], model_loss_last)
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model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
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arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
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[*model_body.output, *y_true])
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model = Model([model_body.input, *y_true], model_loss)
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return model, bottleneck_model, last_layer_model
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def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False):
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'''data generator for fit_generator'''
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n = len(annotation_lines)
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i = 0
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while True:
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image_data = []
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box_data = []
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for b in range(batch_size):
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if i==0 and random:
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np.random.shuffle(annotation_lines)
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image, box = get_random_data(annotation_lines[i], input_shape, random=random)
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image_data.append(image)
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box_data.append(box)
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i = (i+1) % n
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image_data = np.array(image_data)
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if verbose:
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print("Progress: ",i,"/",n)
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box_data = np.array(box_data)
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y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
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yield [image_data, *y_true], np.zeros(batch_size)
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def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False):
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n = len(annotation_lines)
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if n==0 or batch_size<=0: return None
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return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random, verbose)
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def bottleneck_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, bottlenecks):
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n = len(annotation_lines)
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i = 0
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while True:
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box_data = []
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b0=np.zeros((batch_size,bottlenecks[0].shape[1],bottlenecks[0].shape[2],bottlenecks[0].shape[3]))
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b1=np.zeros((batch_size,bottlenecks[1].shape[1],bottlenecks[1].shape[2],bottlenecks[1].shape[3]))
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b2=np.zeros((batch_size,bottlenecks[2].shape[1],bottlenecks[2].shape[2],bottlenecks[2].shape[3]))
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for b in range(batch_size):
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_, box = get_random_data(annotation_lines[i], input_shape, random=False, proc_img=False)
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box_data.append(box)
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b0[b]=bottlenecks[0][i]
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b1[b]=bottlenecks[1][i]
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b2[b]=bottlenecks[2][i]
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i = (i+1) % n
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box_data = np.array(box_data)
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y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
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yield [b0, b1, b2, *y_true], np.zeros(batch_size)
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if __name__ == '__main__':
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_main()
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