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Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
| Date | Stars |
|---|---|
| 2026-07-24 | 1551 |
| 2026-07-25 | 1551 |
| 2026-07-28 | 1551 |
| 2026-07-30 | 1551 |
| 2026-08-06 | 1551 |
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# YOLOv3_TensorFlow **NOTE:** This repo is no longer maintained (actually I dropped the support for a long time) as I have switched to PyTorch for one year. Life is short, I use PyTorch. -------- ### 1. Introduction This is my implementation of [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf) in pure TensorFlow. It contains the full pipeline of training and evaluation on your own dataset. The key features of this repo are: - Efficient tf.data pipeline - Weights converter (converting pretrained darknet weights on COCO dataset to TensorFlow checkpoint.) - Extremely fast GPU non maximum supression. - Full training and evaluation pipeline. - Kmeans algorithm to select prior anchor boxes. ### 2. Requirements Python version: 2 or 3 Packages: - tensorflow >= 1.8.0 (theoretically any version that supports tf.data is ok) - opencv-python - tqdm ### 3. Weights convertion The pretrained darknet weights file can be downloaded [here](https://pjreddie.com/media/files/yolov3.weights). Place this weights file under directory `./data/darknet_weights/` and then run: ```shell python convert_weight.py ``` Then the converted TensorFlow checkpoint file will be saved to `./data/darknet_weights/` directory. You can also download the converted TensorFlow checkpoint file by me via [[Google Drive link](https://drive.google.com/drive/folders/1mXbNgNxyXPi7JNsnBaxEv1-nWr7SVoQt?usp=sharing)] or [[Github Release](https://github.com/wizyoung/YOLOv3_TensorFlow/releases/)] and then place it to the same directory. ### 4. Running demos There are some demo images and videos under the `./data/demo_data/`. You can run the demo by: Single image test demo: ```shell python test_single_image.py ./data/demo_data/messi.jpg ``` Video test demo: ```shell python video_test.py ./data/demo_data/video.mp4 ``` Some results:    Compare the kite detection results with TensorFlow's offical API result [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/img/kites_detections_output.jpg). (The kite detection result is under input image resolution 1344x896) ### 5. Inference speed How fast is the inference speed? With images scaled to 416*416: | Backbone | GPU | Time(ms) | | :-------------------- | :------: | :------: | | Darknet-53 (paper) | Titan X | 29 | | Darknet-53 (my impl.) | Titan XP | ~23 | why is it so fast? Check the ImageNet classification result comparision from the paper:  ### 6. Model architecture For better understanding of the model architecture, you can refer to the following picture. With great thanks to [Levio](https://blog.csdn.net/leviopku/article/details/82660381) for your excellent work!  ### 7. Training #### 7.1 Data preparation (1) annotation file Generate `train.txt/val.txt/test.txt` files under `./data/my_data/` directory. One line for one image, in the format like `image_index image_absolute_path img_width img_height box_1 box_2 ... box_n`. Box_x format: `label_index x_min y_min x_max y_max`. (The origin of coordinates is at the left top corner, left top => (xmin, ymin), right bottom => (xmax, ymax).) `image_index` is the line index which starts from zero. `label_index` is in range [0, class_num - 1]. For example: ``` 0 xxx/xxx/a.jpg 1920 1080 0 453 369 473 391 1 588 245 608 268 1 xxx/xxx/b.jpg 1920 1080 1 466 403 485 422 2 793 300 809 320 ... ``` Since so many users report to use tools like LabelImg to generate xml format annotations, I add one d
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:49bd10261fcb065e, topic:object-detection, readme:object detection
matched fp:49bd10261fcb065e, topic:tensorflow