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Quick Start Guide

Get started with the No-Code Classification Toolkit in minutes!

Quick Start (5 minutes)

1. Prepare Your Dataset

Organize your images in this structure:

my_dataset/
├── Training/
│   ├── cat/
│   │   ├── img1.jpg
│   │   └── img2.jpg
│   └── dog/
│       ├── img1.jpg
│       └── img2.jpg
└── Validation/
    ├── cat/
    │   └── img1.jpg
    └── dog/
        └── img1.jpg

Requirements:

  • At least 100 images per class (configurable)
  • Supported formats: JPG, JPEG, PNG, BMP
  • Two folders: Training and Validation

2. Choose Your Docker Image

For TensorFlow:

docker pull animikhaich/zero-code-classifier:tensorflow

For PyTorch:

docker pull animikhaich/zero-code-classifier:pytorch

For Both:

docker pull animikhaich/zero-code-classifier:both

Or build locally:

git clone https://github.com/animikhaich/No-Code-Classification-Toolkit.git
cd No-Code-Classification-Toolkit
bash build-all.sh

3. Run the Container

# Replace /path/to/my_dataset with your actual dataset path
docker run -it --gpus all --net host \
  -v /path/to/my_dataset:/data \
  animikhaich/zero-code-classifier:pytorch

4. Open the Web Interface

Open your browser and go to: http://localhost:8501

5. Configure Training

  1. Select Framework: Choose TensorFlow or PyTorch (if using "both" image)
  2. Dataset Paths:
    • Training: /data/Training
    • Validation: /data/Validation
  3. Model Settings:
    • Backbone: Start with resnet50 (PyTorch) or ResNet50 (TensorFlow)
    • Optimizer: Adam
    • Learning Rate: 0.001
    • Batch Size: 16 (adjust based on GPU memory)
    • Epochs: 100
    • Image Size: 224
  4. Advanced:
    • Enable Mixed Precision for faster training (if using modern GPU)
  5. Click Start Training!

6. Monitor Training

Watch the live graphs showing:

  • Training/Validation Loss
  • Training/Validation Accuracy
  • Progress bar for each epoch

Training will automatically:

  • Save best model when validation accuracy improves
  • Reduce learning rate when validation plateaus
  • Stop early if no improvement for 10 epochs

7. Get Your Trained Model

After training, copy the model from the container:

For PyTorch:

docker cp <container-id>:/app/model/weights/pytorch ./my_models/

For TensorFlow:

docker cp <container-id>:/app/model/weights/keras ./my_models/

Get TensorBoard logs:

docker cp <container-id>:/app/logs/tensorboard ./logs/

Common Settings

Small Dataset (<1000 images/class)

  • Batch Size: 16
  • Learning Rate: 0.0001
  • Enable augmentation (default)
  • Epochs: 50-100

Medium Dataset (1000-10000 images/class)

  • Batch Size: 32
  • Learning Rate: 0.001
  • Enable augmentation
  • Epochs: 50-100

Large Dataset (>10000 images/class)

  • Batch Size: 64-128
  • Learning Rate: 0.001-0.01
  • Enable augmentation
  • Epochs: 30-50

GPU Memory Issues

If you get out-of-memory errors:

  1. Reduce batch size to 8 or 4
  2. Reduce image size to 192 or 128
  3. Try a smaller model (e.g., mobilenet_v2)

Framework-Specific Tips

PyTorch

  • Models: Use lowercase names (resnet50, mobilenet_v2)
  • Optimizers: Adam, SGD, AdamW
  • Mixed Precision: Check the "Use Mixed Precision (AMP)" box
  • Best for: Research, experimentation, custom modifications

TensorFlow

  • Models: Use TitleCase names (ResNet50, MobileNetV2)
  • Optimizers: Adam, SGD, RMSprop
  • Mixed Precision: Select from dropdown (FP16 for GPU, BF16 for TPU)
  • Best for: Production deployment, TPU training

Troubleshooting

"No module named tensorflow/torch"

  • Make sure you're using the correct Docker image
  • For TensorFlow: use :tensorflow tag
  • For PyTorch: use :pytorch tag

"Data Directory Path is Invalid"

  • Check your dataset path
  • Ensure the path is absolute
  • Verify the directory structure (Training/Validation folders)

Training is too slow

  • Enable mixed precision training
  • Increase batch size if you have GPU memory
  • Use a faster backbone (e.g., MobileNet)
  • Ensure you're using GPU (check --gpus all flag)

Poor accuracy

  • Check your dataset quality
  • Ensure labels are correct
  • Try different learning rates
  • Train for more epochs
  • Use a larger backbone (e.g., ResNet101)

Docker container won't start

  • Ensure Docker is installed
  • For GPU: Install NVIDIA Container Toolkit
  • Check port 8501 is not in use
  • Try without --net host: -p 8501:8501 -p 6006:6006

Advanced Usage

Custom Ports

docker run -it --gpus all \
  -p 8502:8501 \
  -p 6007:6006 \
  -v /path/to/dataset:/data \
  animikhaich/zero-code-classifier:pytorch

Access at: http://localhost:8502

Multiple Datasets

docker run -it --gpus all --net host \
  -v /path/to/dataset1:/data1 \
  -v /path/to/dataset2:/data2 \
  animikhaich/zero-code-classifier:pytorch

Save Models Outside Container

docker run -it --gpus all --net host \
  -v /path/to/dataset:/data \
  -v /path/to/output:/app/model \
  animikhaich/zero-code-classifier:pytorch

Models will be saved directly to /path/to/output

View TensorBoard

Open a new terminal:

docker exec -it <container-id> bash
cd /app
tensorboard --logdir logs/tensorboard --host 0.0.0.0

Access TensorBoard at: http://localhost:6006

Next Steps

  1. Experiment with different backbones and hyperparameters
  2. Compare TensorFlow vs PyTorch performance on your data
  3. Read the Framework Guide for details
  4. Check SECURITY.md for deployment best practices
  5. Review training logs in TensorBoard
  6. Deploy your trained model to production

Need Help?

Happy Training! 🚀