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Yolov3 Object Detection implemented as APIs, using TensorFlow and Flask
| Date | Stars |
|---|---|
| 2026-07-24 | 345 |
| 2026-07-25 | 345 |
| 2026-07-28 | 345 |
| 2026-07-30 | 345 |
| 2026-08-26 | 346 |
| 2026-09-20 | 346 |
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# Yolov3 Object Detection with Flask and Tensorflow 2.0 (APIs and Detections) Yolov3 is an algorithm that uses deep convolutional neural networks to perform object detection. This repository implements Yolov3 using TensorFlow 2.0 and creates two easy-to-use APIs that you can integrate into web or mobile applications. <br>  ## Getting started #### Conda (Recommended) ```bash # Tensorflow CPU conda env create -f conda-cpu.yml conda activate yolov3-cpu # Tensorflow GPU conda env create -f conda-gpu.yml conda activate yolov3-gpu ``` #### Pip ```bash # TensorFlow CPU pip install -r requirements.txt # TensorFlow GPU pip install -r requirements-gpu.txt ``` ### Nvidia Driver (For GPU, if you haven't set it up already) ```bash # Ubuntu 18.04 sudo apt-add-repository -r ppa:graphics-drivers/ppa sudo apt install nvidia-driver-430 # Windows/Other https://www.nvidia.com/Download/index.aspx ``` ### Downloading official pretrained weights For Linux: Let's download official yolov3 weights pretrained on COCO dataset. ``` # yolov3 wget https://pjreddie.com/media/files/yolov3.weights -O weights/yolov3.weights # yolov3-tiny wget https://pjreddie.com/media/files/yolov3-tiny.weights -O weights/yolov3-tiny.weights ``` For Windows: You can download the yolov3 weights by clicking [here](https://pjreddie.com/media/files/yolov3.weights) and yolov3-tiny [here](https://pjreddie.com/media/files/yolov3-tiny.weights) then save them to the weights folder. ### Using Custom trained weights <strong> Learn How To Train Custom YOLOV3 Weights Here: https://www.youtube.com/watch?v=zJDUhGL26iU </strong> Add your custom weights file to weights folder and your custom .names file into data/labels folder. ### Saving your yolov3 weights as a TensorFlow model. Load the weights using `load_weights.py` script. This will convert the yolov3 weights into TensorFlow .ckpt model files! ``` # yolov3 python load_weights.py # yolov3-tiny python load_weights.py --weights ./weights/yolov3-tiny.weights --output ./weights/yolov3-tiny.tf --tiny ``` After executing one of the above lines, you should see .tf files in your weights folder. ## Running the Flask App and Using the APIs Now you can run a Flask application to create two object detections APIs in order to get detections through REST endpoints. If you used custom weights and classes then you may need to adjust one or two of the following lines within the app.py file before running it.  You may also want to configure IOU threshold (how close two of the same class have to be in order to count it as one detection), the Confidence threshold (minimum detected confidence of a class in order to count it as a detection), or the maximum number of classes that can be detected in one image and all three can be adjusted within the yolov3-tf2/models.py file.  Initialize and run the Flask app on port 5000 of your local machine by running the following command from the root directory of this repo in a command prompt or shell. ```bash python app.py ``` You should see the following appear in the command prompt if the app is successfully running.  ### Detections API (http://localhost:5000/detections) While app.py is running the first available API is a POST routed to /detections on port 5000 of localhost. This endpoint takes in images as input and returns a JSON response with all the detections found within each image (classes found within the images and the associated confidence) You can test out the APIs using Postman or through Curl
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d81f3fc70d361f30, topic:object-detection, topic:yolo, name:object detection
matched fp:d81f3fc70d361f30, topic:deep-learning, topic:tensorflow