add fine-tune guide in readme

README.md

@@ -111,3 +111,60 @@ from towhee import ops
 op = ops.image_embedding.isc().get_op()
 op.save_model('onnx', 'test.onnx')
 ```
+
+## Fine-tune
+
+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.
+
+### Get start to fine-tune
+Just call method op.train() and pass in your args.
+```python
+import towhee
+
+op = towhee.ops.image_embedding.isc().get_op()
+op.train(training_args={
+        'train_data_dir': './your_training_images',
+        'distributed': False,
+        'output_dir': './output',
+        'gpu': 0,
+        'epochs': 2,
+        'batch_size': 128,
+        'init_lr': 0.1
+    })
+```
+These are each arg infos in training_args:
+- output_dir 
+  - default: './output'
+  - metadata_dict: {'help': 'output checkpoint saving dir.'} 
+
+- distributed 
+  - default: False 
+  - metadata_dict: {'help': 'If true, use all gpu in your machine, else use only one gpu.'} 
+
+- gpu 
+  - default: 0 
+  - metadata_dict: {'help': 'When distributed is False, use this gpu No. in your machine.'} 
+
+- start_epoch 
+  - default: 0 
+  - metadata_dict: {'help': 'Start epoch number.'} 
+
+- epochs 
+  - default: 6 
+  - metadata_dict: {'help': 'End epoch number.'} 
+
+- batch_size 
+  - default: 128 
+  - metadata_dict: {'help': 'Total batch size in all gpu.'} 
+
+- init_lr 
+  - default: 0.1 
+  - metadata_dict: {'help': 'init learning rate in SGD.'} 
+
+- train_data_dir 
+  - default: None 
+  - metadata_dict: {'help': 'The dir containing all training data image files.'}
+
+### Your custom training
+Your can change [training script](https://towhee.io/image-embedding/isc/src/branch/main/train_isc.py) in your 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.