README.md

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# Image classification example

This directory contains a script, `run_image_classification.py`, that showcases how to fine-tune any model supported by the [`AutoModelForImageClassification` API](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification) (such as [ViT](https://huggingface.co/docs/transformers/main/en/model_doc/vit), [ConvNeXT]((https://huggingface.co/docs/transformers/main/en/model_doc/convnext)), [ResNet]((https://huggingface.co/docs/transformers/main/en/model_doc/resnet)), [Swin Transformer]((https://huggingface.co/docs/transformers/main/en/model_doc/swin))...) using PyTorch. It can be used to fine-tune models on both well-known datasets (like [CIFAR-10](https://huggingface.co/datasets/cifar10), [Fashion MNIST](https://huggingface.co/datasets/fashion_mnist), ...) as well as on your own custom data.

This page includes 2 sections:
- [Using datasets from the hub](#using-datasets-from-🤗-hub)
- [Using your own data](#using-your-own-data).


## Using datasets from 🤗 `Hub`

Here we show how to fine-tune a Vision Transformer (`ViT`) on the [beans](https://huggingface.co/datasets/beans) dataset, to classify the disease type of bean leaves.

👀 See the results here: [nateraw/vit-base-beans](https://huggingface.co/nateraw/vit-base-beans).

```bash
python run_image_classification.py \
    --dataset_name beans \
    --output_dir ./beans_outputs/ \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --push_to_hub \
    --push_to_hub_model_id vit-base-beans \
    --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 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337
```

To fine-tune another model, simply provide the `--model_name_or_path` argument. To train on another dataset, simply set the `--dataset_name` argument. 

👀 See the results here: [nateraw/vit-base-cats-vs-dogs](https://huggingface.co/nateraw/vit-base-cats-vs-dogs).

## Using your own data

To use your own dataset, there are 2 ways: 
- you can either provide your own folders as `--train_dir` and/or `--validation_dir` arguments
- you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.

Below, we explain both in more detail.

### Provide them as folders

If you provide your own folders with images, the 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
```

In other words, you need to organize your images in subfolders, based on their class. You can then run the script like this:

```bash
python run_image_classification.py \
    --train_dir <path-to-train-root> \
    --output_dir ./outputs/ \
    --remove_unused_columns False \
    --do_train \
    --do_eval
```

Internally, the script will use the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature which will automatically turn the folders into 🤗 Dataset objects.

#### 💡 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.

### Upload your data to the hub, as a (possibly private) repo

It's very easy (and convenient) to upload your image dataset to the hub using the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature available in 🤗 Datasets. Simply do the following:

```python
from datasets import load_dataset

# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")

# example 2: local files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")

# example 3: remote files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip")

# example 4: providing several splits
dataset = load_dataset("imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]})
```

`ImageFolder` will create a `label` column, and the label name is based on the directory name.

Next, push it to the hub!

```python
dataset.push_to_hub("name_of_your_dataset")

# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```

and that's it! You can now simply train your model simply by setting the `--dataset_name` argument to the name of your dataset on the hub (as explained in [Using datasets from the hub](#using-datasets-from-🤗-hub)).

More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).

# 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 `huggingface-cli`:

```bash
$ huggingface-cli login
# ...follow the prompts
```

or, in case you're running in a notebook:

```python
from huggingface_hub import notebook_login

notebook_login()
```

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

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