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# Image pretraining examples

This directory contains Python scripts that allow you to pre-train Transformer-based vision models (like [ViT](https://huggingface.co/docs/transformers/model_doc/vit), [Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)) on your own data, after which you can easily load the weights into a [`AutoModelForImageClassification`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForImageClassification). It currently includes scripts for:
- [SimMIM](#simmim) (by Microsoft Research)
- [MAE](#mae) (by Facebook AI).

NOTE: If you encounter problems/have suggestions for improvement, open an issue on Github and tag @NielsRogge.


## SimMIM

The `run_mim.py` script can be used to pre-train any Transformer-based vision model in the library (concretely, any model supported by the `AutoModelForMaskedImageModeling` API) for masked image modeling as proposed in [SimMIM: A Simple Framework for Masked Image Modeling](https://huggingface.co/papers/2111.09886) using PyTorch.

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/simmim_architecture.jpg"
alt="drawing" width="300"/> 

<small> SimMIM framework. Taken from the <a href="https://huggingface.co/papers/2111.09886">original paper</a>. </small>

The goal for the model is to predict raw pixel values for the masked patches, using just a linear layer as prediction head. The model is trained using a simple L1 loss.

### Using datasets from 🤗 datasets

Here we show how to pre-train a `ViT` from scratch for masked image modeling on the [cifar10](https://huggingface.co/datasets/cifar10) dataset.

Alternatively, one can decide to further pre-train an already pre-trained (or fine-tuned) checkpoint from the [hub](https://huggingface.co/). This can be done by setting the `model_name_or_path` argument to "google/vit-base-patch16-224-in21k" for example (and not specifying the `model_type` argument).

```bash
!python run_mim.py \
    --model_type vit \
    --output_dir ./outputs/ \
    --remove_unused_columns False \
    --label_names bool_masked_pos \
    --do_train \
    --do_eval \
    --learning_rate 2e-5 \
    --weight_decay 0.05 \
    --num_train_epochs 100 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --logging_strategy steps \
    --logging_steps 10 \
    --eval_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337
```

Here, we train for 100 epochs with a learning rate of 2e-5. Note that the SimMIM authors used a more sophisticated learning rate schedule, see the [config files](https://github.com/microsoft/SimMIM/blob/main/configs/vit_base__800ep/simmim_pretrain__vit_base__img224__800ep.yaml) for more info. One can easily tweak the script to include this learning rate schedule (several learning rate schedulers are supported via the [training arguments](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments)).

We can also for instance replicate the pre-training of a Swin Transformer using the same architecture as used by the SimMIM authors. For this, we first create a custom configuration and save it locally:

```python
from transformers import SwinConfig

IMAGE_SIZE = 192
PATCH_SIZE = 4
EMBED_DIM = 128
DEPTHS = [2, 2, 18, 2]
NUM_HEADS = [4, 8, 16, 32]
WINDOW_SIZE = 6

config = SwinConfig(
    image_size=IMAGE_SIZE,
    patch_size=PATCH_SIZE,
    embed_dim=EMBED_DIM,
    depths=DEPTHS,
    num_heads=NUM_HEADS,
    window_size=WINDOW_SIZE,
)
config.save_pretrained("path_to_config")
```

Next, we can run the script by providing the path to this custom configuration (replace `path_to_config` below with your path):

```bash
!python run_mim.py \
    --config_name_or_path path_to_config \
    --model_type swin \
    --output_dir ./outputs/ \
    --remove_unused_columns False \
    --label_names bool_masked_pos \
    --do_train \
    --do_eval \
    --learning_rate 2e-5 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --logging_strategy steps \
    --logging_steps 10 \
    --eval_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337
```

This will train a Swin Transformer from scratch.

### Using your own data

To use your own dataset, the training script expects the following directory structure:

```bash
root/dog/xxx.png
root/dog/xxy.png
root/dog/[...]/xxz.png

root/cat/123.png
root/cat/nsdf3.png
root/cat/[...]/asd932_.png
```

Note that you can put images in dummy subfolders, whose names will be ignored by default (as labels aren't required). You can also just place all images into a single dummy subfolder. Once you've prepared your dataset, you can run the script like this:

```bash
python run_mim.py \
    --model_type vit \
    --dataset_name nateraw/image-folder \
    --train_dir <path-to-train-root> \
    --output_dir ./outputs/ \
    --remove_unused_columns False \
    --label_names bool_masked_pos \
    --do_train \
    --do_eval
```

## MAE

The `run_mae.py` script can be used to pre-train a Vision Transformer as a masked autoencoder (MAE), as proposed in [Masked Autoencoders Are Scalable Vision Learners](https://huggingface.co/papers/2111.06377). The script can be used to train a `ViTMAEForPreTraining` model in the Transformers library, using PyTorch. After self-supervised pre-training, one can load the weights of the encoder directly into a `ViTForImageClassification`. The MAE method allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data.

The goal for the model is to predict raw pixel values for the masked patches. As the model internally masks patches and learns to reconstruct them, there's no need for any labels. The model uses the mean squared error (MSE) between the reconstructed and original images in the pixel space.

### Using datasets from 🤗 `datasets`

One can use the following command to pre-train a `ViTMAEForPreTraining` model from scratch on the [cifar10](https://huggingface.co/datasets/cifar10) dataset:

```bash
python run_mae.py \
    --dataset_name cifar10 \
    --output_dir ./vit-mae-demo \
    --remove_unused_columns False \
    --label_names pixel_values \
    --mask_ratio 0.75 \
    --norm_pix_loss \
    --do_train \
    --do_eval \
    --base_learning_rate 1.5e-4 \
    --lr_scheduler_type cosine \
    --weight_decay 0.05 \
    --num_train_epochs 800 \
    --warmup_steps 0.05 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --logging_strategy steps \
    --logging_steps 10 \
    --eval_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337
```

Here we set:
- `mask_ratio` to 0.75 (to mask 75% of the patches for each image)
- `norm_pix_loss` to use normalized pixel values as target (the authors reported better representations with this enabled) 
- `base_learning_rate` to 1.5e-4. Note that the effective learning rate is computed by the [linear schedule](https://huggingface.co/papers/1706.02677): `lr` = `blr` * total training batch size / 256. The total training batch size is computed as `training_args.train_batch_size` * `training_args.gradient_accumulation_steps` * `training_args.world_size`.

This replicates the same hyperparameters as used in the original implementation, as shown in the table below.

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mae_pretraining_setting.png"
alt="drawing" width="300"/> 

<small> Original hyperparameters. Taken from the <a href="https://huggingface.co/papers/2111.06377">original paper</a>. </small>

Alternatively, one can decide to further pre-train an already pre-trained (or fine-tuned) checkpoint from the [hub](https://huggingface.co/). This can be done by setting the `model_name_or_path` argument to "facebook/vit-mae-base" for example.


### Using your own data

To use your own dataset, the training script expects the following directory structure:

```bash
root/dog/xxx.png
root/dog/xxy.png
root/dog/[...]/xxz.png

root/cat/123.png
root/cat/nsdf3.png
root/cat/[...]/asd932_.png
```

Note that you can put images in dummy subfolders, whose names will be ignored by default (as labels aren't required). You can also just place all images into a single dummy subfolder. Once you've prepared your dataset, you can run the script like this:

```bash
python run_mae.py \
    --model_type vit_mae \
    --dataset_name nateraw/image-folder \
    --train_dir <path-to-train-root> \
    --output_dir ./outputs/ \
    --remove_unused_columns False \
    --label_names pixel_values \
    --do_train \
    --do_eval
```

#### 💡 The above will split the train dir into training and evaluation sets
  - To control the split amount, use the `--train_val_split` flag.
  - To provide your own validation split in its own directory, you can pass the `--validation_dir <path-to-val-root>` flag.


## Sharing your model on 🤗 Hub

0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account

1. Make sure you have `git-lfs` installed and git set up.

```bash
$ apt install git-lfs
$ git config --global user.email "you@example.com"
$ git config --global user.name "Your Name"
```

2. Log in with your HuggingFace account credentials using `hf`

```bash
$ hf auth login
# ...follow the prompts
```

3. When running the script, pass the following arguments:

```bash
python run_xxx.py \
    --push_to_hub \
    --push_to_hub_model_id <name-of-your-model> \
    ...
```