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Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
| Date | Stars |
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| 2026-07-24 | 878 |
| 2026-07-25 | 878 |
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# tensorflow-yolo-v3
Implementation of YOLO v3 object detector in Tensorflow (TF-Slim). Full tutorial can be found [here](https://medium.com/@pawekapica_31302/implementing-yolo-v3-in-tensorflow-tf-slim-c3c55ff59dbe).
Tested on Python 3.5, Tensorflow 1.11.0 on Ubuntu 16.04.
## Todo list:
- [x] YOLO v3 architecture
- [x] Basic working demo
- [x] Weights converter (util for exporting loaded COCO weights as TF checkpoint)
- [ ] Training pipeline
- [ ] More backends
## How to run the demo:
To run demo type this in the command line:
1. Download COCO class names file: `wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names`
2. Download and convert model weights:
1. Download binary file with desired weights:
1. Full weights: `wget https://pjreddie.com/media/files/yolov3.weights`
1. Tiny weights: `wget https://pjreddie.com/media/files/yolov3-tiny.weights`
1. SPP weights: `wget https://pjreddie.com/media/files/yolov3-spp.weights`
2. Run `python ./convert_weights.py` and `python ./convert_weights_pb.py`
3. Run `python ./demo.py --input_img <path-to-image> --output_img <name-of-output-image> --frozen_model <path-to-frozen-model>`
####Optional Flags
1. convert_weights:
1. `--class_names`
1. Path to the class names file
2. `--weights_file`
1. Path to the desired weights file
3. `--data_format`
1. `NCHW` (gpu only) or `NHWC`
4. `--tiny`
1. Use yolov3-tiny
5. `--spp`
1. Use yolov3-spp
6. `--ckpt_file`
1. Output checkpoint file
2. convert_weights_pb.py:
1. `--class_names`
1. Path to the class names file
2. `--weights_file`
1. Path to the desired weights file
3. `--data_format`
1. `NCHW` (gpu only) or `NHWC`
4. `--tiny`
1. Use yolov3-tiny
5. `--spp`
1. Use yolov3-spp
6. `--output_graph`
1. Location to write the output .pb graph to
3. demo.py
1. `--class_names`
1. Path to the class names file
2. `--weights_file`
1. Path to the desired weights file
3. `--data_format`
1. `NCHW` (gpu only) or `NHWC`
4. `--ckpt_file`
1. Path to the checkpoint file
5. `--frozen_model`
1. Path to the frozen model
6. `--conf_threshold`
1. Desired confidence threshold
7. `--iou_threshold`
1. Desired iou threshold
8. `--gpu_memory_fraction`
1. Fraction of gpu memory to work with
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c7a2d46e39f80606, topic:deep-learning, topic:tensorflow
matched fp:c7a2d46e39f80606, topic:object-detection, topic:yolo