Image Classification

Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Using AutoTrain, its super-easy to train a state-of-the-art image classification model. Just upload a set of images, and AutoTrain will automatically train a model to classify them.

Data Preparation

The data for image classification must be in zip format, with each class in a separate subfolder. For example, if you want to classify cats and dogs, your zip file should look like this:

cats_and_dogs.zip
├── cats
│   ├── cat.1.jpg
│   ├── cat.2.jpg
│   ├── cat.3.jpg
│   └── ...
└── dogs
    ├── dog.1.jpg
    ├── dog.2.jpg
    ├── dog.3.jpg
    └── ...

Some points to keep in mind:

The zip file should contain multiple folders (the classes), each folder should contain images of a single class.
The name of the folder should be the name of the class.
The images must be jpeg, jpg or png.
There should be at least 5 images per class.
There should not be any other files in the zip file.
There should not be any other folders inside the zip folder.

When train.zip is decompressed, it creates two folders: cats and dogs. these are the two categories for classification. The images for both categories are in their respective folders. You can have as many categories as you want.

Parameters

❯ autotrain image-classification --help
usage: autotrain <command> [<args>] image-classification [-h] [--train] [--deploy] [--inference] [--username USERNAME]
                                                         [--backend {local-cli,spaces-a10gl,spaces-a10gs,spaces-a100,spaces-t4m,spaces-t4s,spaces-cpu,spaces-cpuf}]
                                                         [--token TOKEN] [--push-to-hub] --model MODEL --project-name PROJECT_NAME
                                                         [--data-path DATA_PATH] [--train-split TRAIN_SPLIT] [--valid-split VALID_SPLIT]
                                                         [--batch-size BATCH_SIZE] [--seed SEED] [--epochs EPOCHS]
                                                         [--gradient_accumulation GRADIENT_ACCUMULATION] [--disable_gradient_checkpointing]
                                                         [--lr LR] [--log {none,wandb,tensorboard}] [--image-column IMAGE_COLUMN]
                                                         [--target-column TARGET_COLUMN] [--warmup-ratio WARMUP_RATIO] [--optimizer OPTIMIZER]
                                                         [--scheduler SCHEDULER] [--weight-decay WEIGHT_DECAY] [--max-grad-norm MAX_GRAD_NORM]
                                                         [--logging-steps LOGGING_STEPS] [--evaluation-strategy {steps,epoch,no}]
                                                         [--save-total-limit SAVE_TOTAL_LIMIT] [--save-strategy {steps,epoch,no}]
                                                         [--auto-find-batch-size] [--mixed-precision {fp16,bf16,None}]

✨ Run AutoTrain Image Classification

options:
  -h, --help            show this help message and exit
  --train               Command to train the model
  --deploy              Command to deploy the model (limited availability)
  --inference           Command to run inference (limited availability)
  --username USERNAME   Hugging Face Hub Username
  --backend {local-cli,spaces-a10gl,spaces-a10gs,spaces-a100,spaces-t4m,spaces-t4s,spaces-cpu,spaces-cpuf}
                        Backend to use: default or spaces. Spaces backend requires push_to_hub & username. Advanced users only.
  --token TOKEN         Your Hugging Face API token. Token must have write access to the model hub.
  --push-to-hub         Push to hub after training will push the trained model to the Hugging Face model hub.
  --model MODEL         Base model to use for training
  --project-name PROJECT_NAME
                        Output directory / repo id for trained model (must be unique on hub)
  --data-path DATA_PATH
                        Train dataset to use. When using cli, this should be a directory path containing training and validation data in appropriate
                        formats
  --train-split TRAIN_SPLIT
                        Train dataset split to use
  --valid-split VALID_SPLIT
                        Validation dataset split to use
  --batch-size BATCH_SIZE
                        Training batch size to use
  --seed SEED           Random seed for reproducibility
  --epochs EPOCHS       Number of training epochs
  --gradient_accumulation GRADIENT_ACCUMULATION
                        Gradient accumulation steps
  --disable_gradient_checkpointing
                        Disable gradient checkpointing
  --lr LR               Learning rate
  --log {none,wandb,tensorboard}
                        Use experiment tracking
  --image-column IMAGE_COLUMN
                        Image column to use
  --target-column TARGET_COLUMN
                        Target column to use
  --warmup-ratio WARMUP_RATIO
                        Define the proportion of training to be dedicated to a linear warmup where learning rate gradually increases. This can help
                        in stabilizing the training process early on. Default ratio is 0.1.
  --optimizer OPTIMIZER
                        Choose the optimizer algorithm for training the model. Different optimizers can affect the training speed and model
                        performance. 'adamw_torch' is used by default.
  --scheduler SCHEDULER
                        Select the learning rate scheduler to adjust the learning rate based on the number of epochs. 'linear' decreases the
                        learning rate linearly from the initial lr set. Default is 'linear'. Try 'cosine' for a cosine annealing schedule.
  --weight-decay WEIGHT_DECAY
                        Set the weight decay rate to apply for regularization. Helps in preventing the model from overfitting by penalizing large
                        weights. Default is 0.0, meaning no weight decay is applied.
  --max-grad-norm MAX_GRAD_NORM
                        Specify the maximum norm of the gradients for gradient clipping. Gradient clipping is used to prevent the exploding gradient
                        problem in deep neural networks. Default is 1.0.
  --logging-steps LOGGING_STEPS
                        Determine how often to log training progress. Set this to the number of steps between each log output. -1 determines logging
                        steps automatically. Default is -1.
  --evaluation-strategy {steps,epoch,no}
                        Specify how often to evaluate the model performance. Options include 'no', 'steps', 'epoch'. 'epoch' evaluates at the end of
                        each training epoch by default.
  --save-total-limit SAVE_TOTAL_LIMIT
                        Limit the total number of model checkpoints to save. Helps manage disk space by retaining only the most recent checkpoints.
                        Default is to save only the latest one.
  --save-strategy {steps,epoch,no}
                        Determine the strategy for saving model checkpoints. Possible values are 'no', 'steps', 'epoch'. 'epoch' saves a checkpoint
                        at the end of each epoch by default.
  --auto-find-batch-size
                        Enable automatic batch size determination based on your hardware capabilities. When set, it tries to find the largest batch
                        size that fits in memory.
  --mixed-precision {fp16,bf16,None}
                        Choose the precision mode for training to optimize performance and memory usage. Options are 'fp16', 'bf16', or None for
                        default precision. Default is None.

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