Python库 | yolov4-0.24.0.tar.gz

   # tensorflow-yolov4 ```shell python3 -m pip install yolov4 ``` YOLOv4 Implemented in Tensorflow 2. ## Download Weights - [yolov4-tiny.conv.29](https://drive.google.com/file/d/1WtOuGfUgNyNfALo5_VhQ1kb5QenRE0Gt/view?usp=sharing) - [yolov4-tiny.weights](https://drive.google.com/file/d/1GJwGiR7rizY_19c_czuLN8p31BwkhWY5/view?usp=sharing) - [yolov4.conv.137](https://drive.google.com/file/d/1li1pUtqpXj_-ZXxA8wJq-nzW8h2HWsrP/view?usp=sharing) - [yolov4.weights](https://drive.google.com/file/d/15P4cYyZ2Sd876HKAEWSmeRdFl_j-0upi/view?usp=sharing) ## Dependencies ```shell python3 -m pip install -U pip setuptools wheel ``` ```shell python3 -m pip install numpy ``` Install OpenCV (cv2) ### Tensorflow 2 ```shell python3 -m pip install tensorflow ``` ### TFlite Ref: [https://www.tensorflow.org/lite/guide/python](https://www.tensorflow.org/lite/guide/python) ## Objective - [x] Train and predict using TensorFlow 2 only - [x] Run yolov4-tiny-relu on Coral board(TPU). - [ ] Train tiny-relu with coco 2017 dataset - [ ] Update Docs - [ ] Optimize model and operations ## Performance   ## Help ```python >>> from yolov4.tf import YOLOv4 >>> help(YOLOv4) ``` ## Inference ### tensorflow ```python from yolov4.tf import YOLOv4 yolo = YOLOv4() yolo.classes = "coco.names" yolo.make_model() yolo.load_weights("yolov4.weights", weights_type="yolo") yolo.inference(media_path="kite.jpg") yolo.inference(media_path="road.mp4", is_image=False) ``` [Object detection test jupyter notebook](./test/object_detection_in_image.ipynb) ```python from yolov4.tf import YOLOv4 yolo = YOLOv4(tiny=True) yolo.classes = "coco.names" yolo.make_model() yolo.load_weights("yolov4-tiny.weights", weights_type="yolo") yolo.inference(media_path="kite.jpg") yolo.inference(media_path="road.mp4", is_image=False) ``` ### tensorflow lite ```python from yolov4.tf import YOLOv4 yolo = YOLOv4() yolo.classes = "coco.names" yolo.make_model() yolo.load_weights("yolov4.weights", weights_type="yolo") yolo.save_as_tflite("yolov4.tflite") ``` ```python from yolov4.tflite import YOLOv4 yolo = YOLOv4() yolo.classes = "coco.names" yolo.load_tflite("yolov4.tflite") yolo.inference("kite.jpg") ``` ## Training ```python from tensorflow.keras import callbacks, optimizers from yolov4.tf import SaveWeightsCallback, YOLOv4 yolo = YOLOv4(tiny=True) yolo.classes = "coco.names" yolo.input_size = 608 yolo.batch_size = 32 yolo.subdivision = 16 yolo.make_model() yolo.load_weights("yolov4-tiny.conv.29", weights_type="yolo") train_data_set = yolo.load_dataset("train2017.txt") val_data_set = yolo.load_dataset("val2017.txt", training=False) # data_set = yolo.load_dataset("darknet/data/train.txt", dataset_type="yolo") lr = 1e-4 epochs = 30000 optimizer = optimizers.Adam(learning_rate=lr) yolo.compile(optimizer=optimizer, loss_iou_type="ciou") def lr_scheduler(epoch): if epoch < 1000: return (epoch / 1000) * lr elif epoch < int(epochs * 0.8): return lr elif epoch < int(epochs * 0.9): return lr * 0.1 else: return lr * 0.01 yolo.fit( train_data_set, epochs=epochs, callbacks=[ callbacks.LearningRateScheduler(lr_scheduler), callbacks.TerminateOnNaN(), callbacks.TensorBoard( log_dir="/content/drive/My Drive/Hard_Soft/NN/logs", ), SaveWeightsCallback( yolo=yolo, weights_type="yolo", epoch_per_save=1000 ), ], validation_data=val_data_set, validation_steps=100, validation_freq=100, ) ``` [Custom training on Colab jupyter notebook](./test/custom_training_on_colab.ipynb)