bird_watch:基于深度学习的鸟类图像识别系统

# Bird Watch A Deep Learning based Bird Image Identification System, using Keras, TensorFlow, OpenCV, and Flask. ## Introduction The *'Bird Watch'* project, created by an amateur photographer and a machine learning enthusiast, is a solution to a simple problem faced by fellow wildlife photographers: a way to identify birds in photographs. The application is developed using Keras and TensorFlow, with Flask for the web application. InceptionV3 was used as the base model and was trained using transfer learning and fine-tuning techniques. The live application can be found at [https://www.birdwatch.photo/](https://www.birdwatch.photo/) ## Usage ### Setup The libraries required to run the Flask app can be installed via the following commands. Using PIP: ```bash pip install -r requirements.txt ``` Using Conda: ```bash conda install numpy scipy h5py Pillow Click Flask itsdangerous Jinja2 MarkupSafe Werkzeug tensorflow pip install keras ``` Note: You can install `tensorflow-gpu` (instead of tensorflow) if you have a CUDA capable GPU. ### Running the App First, head over to the [Releases page](https://github.com/Thimira/bird_watch/releases/latest) and grab the latest `final_model_*.h5` and `class_indices_*.npy` files, and place them in the `models` directory. You can then start the Flask app can be run by running, ```bash python application.py ``` The app would by default run on `http://127.0.0.1:5000/` ### Training with your own data In order to train with you own images, create a `data/train` directory and place your images within sub-directories for each class within the train directory (as required by the flow_from_directory function of Keras: [https://keras.io/preprocessing/image/](https://keras.io/preprocessing/image/) ). Create a `data/models` directory for the bottleneck features and the trained models to be saved. You can also create a `data/eval` directory and place few sample images there to evaluate the model after training. Once you have the data ready, you can run, ```bash python bird_watch_train.py ``` This will run the combined training and fine-tuning script which will generate the final model files. Note: The training may take 10+ hours to run, even on a GPU such as a RTX 2070. Once the training is over, you will have `final_model_*.h5` and `class_indices_*.npy` in your `data/models` directory. Copy them over to your top level `models` directory and you'll be good to go. ## Dependencies ### Runtime The main requirements to run the Flask application are: - TensorFlow - Keras - Flask The full set of runtime dependencies are in the requirements.txt ### Training In order to re-train the model, the following additional libs are needed: - OpenCV - Matplotlib - Pillow ## Author - [Thimira Amaratunga](https://github.com/Thimira)