remove train.py

README.md

@@ -21,50 +21,51 @@ Please note that this dataset is **NOT** intended for clinical use.
 # Code Structure
 * [`medmnist/`](medmnist/):
     * [`dataset.py`](medmnist/dataset.py): PyTorch datasets and dataloaders of MedMNIST.
-    * [`models.py`](medmnist/models.py): *ResNet-18* and *ResNet-50* models.
     * [`evaluator.py`](medmnist/evaluator.py): Standardized evaluation functions.
     * [`info.py`](medmnist/info.py): Dataset information `dict` for each subset of MedMNIST.
-* [`train.py`](train.py): The training and evaluation script to reproduce the baseline results in the paper.
-* [`getting_started.ipynb`](getting_started.ipynb): Explore the MedMNIST dataset with jupyter notebook. It is **ONLY** intended for a quick exploration, i.e., it does not provide full training and evaluation functionalities (please refer to [`train.py`](train.py) instead). 
+* [`examples/`](examples/):
+    * [`getting_started.ipynb`](examples/getting_started.ipynb): Explore the MedMNIST dataset with jupyter notebook. It is **ONLY** intended for a quick exploration, i.e., it does not provide full training and evaluation functionalities. Please refer to our another repository [`MedMNIST/experiments`](https://github.com/MedMNIST/experiments) for all experiments, including PyTorch, auto-sklearn, AutoKeras and Google AutoML Vision, together with their weights!
+    * [`getting_started_without_PyTorch.ipynb`](examples/getting_started_without_PyTorch.ipynb): This notebook provides snippets about how to use MedMNIST data (the `.npz` files) without PyTorch.
 * [`setup.py`](setup.py): The script to install medmnist as a module
 
-# Requirements
-The code requires only common Python environments for machine learning; Basicially, it was tested with
+# Installation and Requirements
+Setup the required environments and install `medmnist` as a standard Python package:
+
+        pip install git+https://github.com/MedMNIST/MedMNIST.git
+
+
+The code requires only common Python environments for machine learning. Basicially, it was tested with
 * Python 3 (Anaconda 3.6.3 specifically)
 * PyTorch\==0.3.1
-* numpy\==1.18.5, pandas\==0.25.3, scikit-learn\==0.22.2, tqdm, Pillow
+* numpy\==1.18.5, pandas\==0.25.3, scikit-learn\==0.22.2, Pillow\==8.0.1, fire
+
+Higher (or lower) versions should also work (perhaps with minor modifications). 
 
-Higher (or lower) versions should also work (perhaps with minor modifications).
+It is suggested to use our PyTorch [`dataset`](medmnist/dataset.py) code to parse the `.npz` files; However, you are free to parse them with your own code (without PyTorch or even without Python!), as they are only standard NumPy serialization files. Please refer to [`getting_started_without_PyTorch.ipynb`](examples/getting_started_without_PyTorch.ipynb), which provides snippets about how to use MedMNIST data (the `.npz` files) without PyTorch.
 
 
 # Dataset
 
 Please download the dataset(s) via [`Zenodo`](https://doi.org/10.5281/zenodo.4269852). You could also use our code to download automatically.
 
-It is suggested to use our [`dataset`](medmnist/dataset.py) code to parse the `.npz` files; however, you are free to parse them with your own code (including but not limited to Python), as they are only standard NumPy serialization files. 
-
-The dataset contains several subsets, and each subset (e.g., `pathmnist.npz`) is comprised of 6 keys: `train_images`, `train_labels`, `val_images`, `val_labels`, `test_images` and `test_labels`.
+The MedMNIST dataset contains several subsets. Each subset (e.g., `pathmnist.npz`) is comprised of 6 keys: `train_images`, `train_labels`, `val_images`, `val_labels`, `test_images` and `test_labels`.
 * `train_images` / `val_images` / `test_images`: `N` x 28 x 28 x 3 for RGB,  `N` x 28 x 28 for gray-scale. `N` denotes the number of samples.  
 * `train_labels` / `val_labels` / `test_labels`: `N` x `L`. `N` denotes the number of samples. `L` denotes the number of task labels; for single-label (binary/multi-class) classification, `L=1`, and `{1,2,3,4,5,..,C}` denotes the category labels (`C=2` for binary); for multi-label classification `L!=1`, e.g., `L=14` for `chestmnist.npz`.
 
-# How to run the experiments
+# How to Use
 
-* Download the dataset manually or automatically (by setting `download=True` in [`dataset.py`](medmnist/dataset.py)).
+* List all available datasets
+
+    python -m medmnist list
 
-* [recommended] Install medmnist as a module by using command `python setup.py install`
+* Download the dataset manually or automatically (by setting `download=True` in [`dataset.py`](medmnist/dataset.py)).
 
-* Run the demo code [`train.py`](./train.py) script in terminal. 
+* Please refer to our another repository [`MedMNIST/experiments`](https://github.com/MedMNIST/experiments) for all experiments, including PyTorch, auto-sklearn, AutoKeras and Google AutoML Vision together with their weights!
 
-  First, change directory to where [`train.py`](./train.py) locates. Then, use command `python train.py --data_name xxxmnist --input_root input --output_root output --num_epoch 100 --download True` to run the experiments, where `xxxmnist` is subset of our MedMNIST (e.g., `pathmnist`), `input` is the path of the data files, `output` is the folder to save the results, `num_epoch` is the number of epochs of training, and `download` is the bool value whether download the dataset. 
-
-  For instance, to run PathMNIST
-
-      python train.py --data_name pathmnist --input_root <path/to/input/folder> --output_root <path/to/output/folder> --num_epoch 100 --download True
-
 # Citation
 If you find this project useful, please cite our paper as:
 
-      Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.
+    Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.
 
 or using bibtex: