https://github.com/carefree0910/imagerecognition
An Image Recognition project using Inception-v3 (for training) and cv2 (for visualizing)
https://github.com/carefree0910/imagerecognition
image-recognition tensorflow-experiments
Last synced: 4 months ago
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An Image Recognition project using Inception-v3 (for training) and cv2 (for visualizing)
- Host: GitHub
- URL: https://github.com/carefree0910/imagerecognition
- Owner: carefree0910
- Created: 2017-03-22T06:19:09.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-04-28T08:54:19.000Z (almost 9 years ago)
- Last Synced: 2025-01-31T02:44:13.820Z (about 1 year ago)
- Topics: image-recognition, tensorflow-experiments
- Language: Python
- Homepage:
- Size: 43 KB
- Stars: 6
- Watchers: 3
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[1]:https://github.com/carefree0910/MachineLearning/tree/master/_Dist/ImageRecognition "MachineLearning"
[2]:https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip "Inception-v3"
# Image Recognition
Dependency: numpy, matplotlib, Tensorflow, cv2(For visualization)
A Stand-alone version for ImageRecognition project [here][1]
+ Inception-v3 model for this project can be downloaded [here][2]
+ Extract the zipped file and put Inception-v3 model (which should be renamed from 'tensorflow_inception_graph.pb' to 'Model.pb') to 'Models/Extractor/v3' folder
+ Put your training set **FOLDERS** into '_Data' folder, please use English names for your folders to ensure that cv2 works correctly
+ Each folder name should be treated as the 'label' of the pictures contained in the folder
+ Put your test set **PICTURES** into 'Test' folder
+ If possible, provide a **ONE-HOT** answer naming '_answer.npy' into 'Test' folder as well for better visualization
+ If you don't want to struggling for these, just leave 'Test' folder empty (Reference the Notice below)
+ Run 'Main.py'!
## Notice That:
+ If 'Test' folder remains empty when the program is running, `min(196, 0.2 * n_data)`pictures will be **MOVED** from '_Data' folder to 'Test' folder if 'gen_test' FLAG is True
+ An '_answer.npy' ndarray will also be generated automatically!
+ After processing all images in '_Data' folder, a '_Cache' folder which contains 'features.npy' and 'labels.npy' (shuffled) will be generated
+ If you want to train on new dataset, '_Cache' folder should be deleted
+ You can train your own classifier using 'features.npy' and 'labels.npy'
+ After the program is done, a Predictor will be stored in 'Models/Predictors/v3' folder. If you want to train on new dataset, this folder should be deleted
--args:
parser.add_argument(
"--gen_test",
type=bool,
default=True,
help="Whether generate test images"
)
parser.add_argument(
"--images_dir",
type=str,
default="Test",
help="Path to test set"
)
parser.add_argument(
"--extract_only",
type=bool,
default=False,
help="Whether extract features only"
)
parser.add_argument(
"--visualize_only",
type=bool,
default=False,
help="Whether visualize only"
)
parser.add_argument(
"--overview",
type=bool,
default=True,
help="Whether use cv2 to overview"
)
## Visualization
*(Not so elegant, but (maybe) better than nothing...)*