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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]
[--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.
--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.