https://github.com/virajmavani/yolo-tensorflow-object-detection
YOLO Object Detection using Tensorflow
https://github.com/virajmavani/yolo-tensorflow-object-detection
deep-learning object-detection python tensorflow yolo
Last synced: 2 months ago
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YOLO Object Detection using Tensorflow
- Host: GitHub
- URL: https://github.com/virajmavani/yolo-tensorflow-object-detection
- Owner: virajmavani
- License: mit
- Created: 2018-04-20T08:18:42.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-04-20T08:58:17.000Z (about 8 years ago)
- Last Synced: 2025-04-11T02:25:12.598Z (about 1 year ago)
- Topics: deep-learning, object-detection, python, tensorflow, yolo
- Language: Python
- Size: 20.5 MB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YOLO-Object-Detection-Tensorflow
YOLO: Real-Time Object Detection using Tensorflow and easy to use.
## Information
* I will get back soon for the issues, pretty busy.
* I found it this YOLO not able to train.
## Results
## Instructions
1. Make sure check settings.py before start to train
```python
# remove elements that you don't want
# default objects from VOC 2012
classes_name = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
classes_no = [i for i in xrange(len(classes_name))]
classes_dict = dict(zip(classes_name, classes_no))
image_size = 448
cell_size = 7
box_per_cell = 2
alpha_relu = 0.2
object_scale = 2.0
no_object_scale = 1.0
class_scale = 2.0
coordinate_scale = 5.0
flipped = True
decay_step = 30000
decay_rate = 0.1
learning_rate = 0.0001
dropout = 0.5
batch_size = 3
epoch = 1000
checkpoint = 1000
# For main
threshold = 0.2
IOU_threshold = 0.5
test_percentage = 0.05
# 1 for read a picture
# 2 to read from testing dataset
# 3 to read from webcam / video
output = 1
# let empty if want to capture from webcam
picture_name = ''
video_name = ''
```
2. You must download [VOC 2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) and put in the same folder for multibox dataset
3. You need to put [YOLO_small.ckpt](https://drive.google.com/open?id=0BxQQlrLbdunWSTZjaHVwUVVOUXM) in the same folder (optional) if you want to use pretrained model, but if you want to train the model by your own, follow 3.*
1. You can train your own model from scratch in train-classification folder
2. Put your images that you want to classify in train-classification/data/ (need to create it)
3. If you satisfied enough your model, copy model.ckpt into main folder
```
/
/train-classification
/train-classification/data/
/train-classification/data/fish/
/train-classification/data/cow/
.. so on
```
```bash
python train.py
```
4. you must train.py in main directory first before main.py (unless if you downloaded YOLO_small.ckpt)
```bash
python train.py
```
5. test in main
```bash
python main.py
```
## More results
## Download Extras and put them in root first:
https://drive.google.com/drive/folders/1xJ1xoZxWhPQp0NY5QU3bFdQhv5Z-w3G_?usp=sharing