diff --git a/examples/examples_image-classification/README.md b/examples/examples_image-classification/README.md
index 08e51bf..64331b3 100644
--- a/examples/examples_image-classification/README.md
+++ b/examples/examples_image-classification/README.md
@@ -28,9 +28,7 @@ pip install transformers==4.16.2
 pip install datsets==1.18.0
 ```
 
-### 2. upgrade the transformers to 4.10.0
-
-### 3. run
+### 2. run
 ```bash
 python run_image_classification.py configs/lora_beans.json
 ```
@@ -46,121 +44,7 @@ Solution 1: open a python console, running the error command again, may not be u
 Solution 2: download the dataset by yourself on a internect connected machine, saved to disk and transfer to your server, at last load_from_disk.
 
 
+## Link to original training scripts
+You may find solution to other question about the scripts and irrelevant to Opendelta in 
+https://github.com/huggingface/transformers/tree/master/examples/pytorch/image-classification
 
-# Image classification examples
-
-The following examples showcase how to fine-tune a `ViT` for image-classification using PyTorch.
-
-## Using datasets from 🤗 `datasets`
-
-Here we show how to fine-tune a `ViT` on the [beans](https://huggingface.co/datasets/beans) dataset.
-
-👀 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
-```
-
-Here we show how to fine-tune a `ViT` on the [cats_vs_dogs](https://huggingface.co/datasets/cats_vs_dogs) dataset.
-
-👀 See the results here: [nateraw/vit-base-cats-vs-dogs](https://huggingface.co/nateraw/vit-base-cats-vs-dogs).
-
-```bash
-python run_image_classification.py \
-    --dataset_name cats_vs_dogs \
-    --output_dir ./cats_vs_dogs_outputs/ \
-    --remove_unused_columns False \
-    --do_train \
-    --do_eval \
-    --push_to_hub \
-    --push_to_hub_model_id vit-base-cats-vs-dogs \
-    --fp16 True \
-    --learning_rate 2e-4 \
-    --num_train_epochs 5 \
-    --per_device_train_batch_size 32 \
-    --per_device_eval_batch_size 32 \
-    --logging_strategy steps \
-    --logging_steps 10 \
-    --evaluation_strategy epoch \
-    --save_strategy epoch \
-    --load_best_model_at_end True \
-    --save_total_limit 3 \
-    --seed 1337
-```
-
-## 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
-```
-
-Once you've prepared your dataset, you can can run the script like this:
-
-```bash
-python run_image_classification.py \
-    --dataset_name nateraw/image-folder \
-    --train_dir <path-to-train-root> \
-    --output_dir ./outputs/ \
-    --remove_unused_columns False \
-    --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 `huggingface-cli`
-
-```bash
-$ huggingface-cli login
-# ...follow the prompts
-```
-
-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> \
-    ...
-```
\ No newline at end of file