Banana Recognizer 🍌
Processing power used:
- GPU: 2x NVIDIA Tesla V100 32GB
- CPU: 2x Intel Xeon Gold 6132 2.6G, 14C/28T, 10.4GT/s 2UPI, 19M Cache, Turbo, HT (140W) DDR4-2666
- RAM: 24x 32GB = 768GB
Convolutional Neural Network: `BananaNetwork/banana_net_17_12_18.h5
INPUT > CONV > RELU > POOL > FC > RELU > FC- Build script: `CNN.py
Convolutional Netural Network: BananaNetwork/le_banana_net_26_02_19.h5
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INPUT > CONV > RELU > POOL > CONV > RELU > POOL > FC > RELU > FC -
Build script:
CNN_LeNet.py -
Also looking at other alternatives for design of network
File structure
├── BananaNetwork
│ ├── banana_net.h5
│ ├── banana_net_17_12_18.h5
│ ├── banana_net_26_02_19.h5
│ └── le_banana_net.h5
├── CNN.py
├── CNN_LeNet.py
├── README.md
├── WebcamApp
│ └── camera_app.py
├── banana_predicter.py
├── datasets
│ ├── google_images
│ │ └── ...
│ ├── google_synset_fruit365
│ │ └── ...
│ └── synset_fruit365
│ └── ...
├── epoch_fig.png
├── google_images_scraper.js
├── gui
│ ├── gui.py
│ └── gui.ui
├── image_downloader.py
├── network_trainer.py
├── predict_tests
│ └── ...
└── urls
├── google_banana_urls.txt
└── synset_banana_urls.txt
Datasets
- Fruit365
- Synset - ImageNet
- Google Images
Latest training
