From 70f235293a9bf5298a76f1c8b881df17ead3ce84 Mon Sep 17 00:00:00 2001
From: ChengZi <chen.zhang@zilliz.com>
Date: Mon, 20 Feb 2023 11:20:08 +0800
Subject: [PATCH] refine isc training in readme

---
 README.md | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 69d16b6..133cd84 100644
--- a/README.md
+++ b/README.md
@@ -101,13 +101,22 @@ op.save_model('onnx', 'test.onnx')
 
 ### Requirements
 
-If you want to fine tune this operator, make sure your timm version is 0.4.12, and the higher version would cause model collapse during training.
+If you want to fine tune this operator, make sure your timm version is 0.4.12, and the higher version may cause model collapse during training.
 ```python
 ! python -m pip install tqdm augly timm==0.4.12 pytorch-metric-learning==0.9.99
 ```
 
 ### Download dataset
-Your need to download dataset from [Facebook AI Image Similarity Challenge: Descriptor Track](https://www.drivendata.org/competitions/79/competition-image-similarity-1-dev/), and use the `./training_images` folder as your training images root. It requires about 165G space.
+ISC is trained using [contrastive learning](https://lilianweng.github.io/posts/2021-05-31-contrastive/), which is a type of self-supervised training. The training images do not require any labels. We only need to prepare a folder `./training_images`, under which a large number of diverse training images can be stored.
+
+In the original training of [ISC21-Descriptor-Track-1st](https://github.com/lyakaap/ISC21-Descriptor-Track-1st), the training dataset is a huge dataset which takes more than 165G space. And it uses [multi-steps training strategy](https://arxiv.org/abs/2104.00298).
+
+In our fine-tune example, to simplification, we prepare a small dataset to run, and you can replace it with your own custom dataset.
+
+```python
+! curl -L https://github.com/towhee-io/examples/releases/download/data/isc_training_image_examples.zip -o ./training_images.zip
+! unzip -q -o ./training_images.zip
+```
 
 ### Get started to fine-tune
 Just call method op.train() and pass in your args.
@@ -121,7 +130,7 @@ op.train(training_args={
         'output_dir': './output',
         'gpu': 0,
         'epochs': 2,
-        'batch_size': 128,
+        'batch_size': 8,
         'init_lr': 0.1
     })
 ```
@@ -158,6 +167,11 @@ These are each arg infos in training_args:
   - default: None 
   - metadata_dict: {'help': 'The dir containing all training data image files.'}
 
+### Load trained model
+```python
+new_op = towhee.ops.image_embedding.isc(checkpoint_path='./output/checkpoint_epoch0001.pth.tar').get_op()
+```
+
 ### Your custom training
-Your can change [training script](https://towhee.io/image-embedding/isc/src/branch/main/train_isc.py) in your way. 
+Your can change [training script](https://towhee.io/image-embedding/isc/src/branch/main/train_isc.py) in your custom way. 
 Or your can refer to the [original repo](https://github.com/lyakaap/ISC21-Descriptor-Track-1st) and [paper](https://arxiv.org/abs/2112.04323) to learn more about contrastive learning and image instance retrieval.
\ No newline at end of file