Update README.md

README.md

@@ -85,7 +85,7 @@ from PaDT import PaDTForConditionalGeneration, VisonTextProcessingClass, parseVR
 
 
 TEST_IMG_PATH="./eval/imgs/000000368335.jpg"
-MODEL_PATH="PaDT-MLLM/PaDT_Pro_3B"
+MODEL_PATH="PaDT-MLLM/PaDT_REC_3B"
 
 # load model
 model = PaDTForConditionalGeneration.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16, device_map={"": 0})
@@ -97,7 +97,7 @@ processor = VisonTextProcessingClass(processor, model.config.vision_config.spati
 processor.prepare(model.model.embed_tokens.weight.shape[0])
 
 # question prompt
-PROMPT = "Please describe this image."
+PROMPT = """Please carefully check the image and detect the object this sentence describes: "The car is on the left side of the horse"."""
 
 # construct conversation
 message = [
@@ -187,6 +187,65 @@ Here are some randomly selected test examples showcasing PaDT’s excellent perf
 <img src="./assets/TAM.webp" width="900"/>
 </div>
 
+## Training Instruction
+
+Download Datasets:
+
+- [COCO](https://cocodataset.org/#home)
+
+- RefCOCO/+/g
+    ```bash
+    wget https://web.archive.org/web/20220413011718/https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
+    wget https://web.archive.org/web/20220413011656/https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
+    wget https://web.archive.org/web/20220413012904/https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip
+    ```
+
+Unpack these datasets and place them under the following directory:
+
+```
+PaDT/
+ ├── dataset/
+ │    ├── coco/
+ │    │     ├── annotations/
+ │    │     ├── train2014/
+ │    │     ├── train2017/
+ │    │     ├── val2014/
+ │    │     └── val2017/
+ │    └── RefCOCO/
+ │          ├── refcoco/
+ │          ├── refcoco+/
+ │          └── refcocog/
+```
+
+Preprocess the datasets:
+- 1. Preprocess via our scripts. (Please first update the dataset path configuration in the preprocessing scripts)
+    ```bash
+    cd src/preprocess
+    python process_coco.py
+    python process_refcoco.py
+    ```
+- 2. We also released the preprocessed datasets which are ready to use for training in huggingface.
+
+    | Dataset | Dataset Path | Task Type |
+    | - | - | -|
+    | COCO | [PaDT-MLLM/COCO](https://huggingface.co/datasets/PaDT-MLLM/COCO) | Open Vocabulary Detection |
+    | RefCOCO | [PaDT-MLLM/RefCOCO](https://huggingface.co/datasets/PaDT-MLLM/RefCOCO) | Referring Expression Comprehension/Segmentation |
+    | RIC | [PaDT-MLLM/ReferringImageCaptioning](https://huggingface.co/datasets/PaDT-MLLM/ReferringImageCaptioning) | Referring Image Captioning |
+
+
+The training scripts in `run_scripts` are ready to execute.
+
+For example: Train the PaDT-Pro 3B model on a single node with 8×96 GB GPUs.
+
+```bash
+bash ./run_scripts/padt_pro_3b_sft.sh
+```
+
+## Evaluation
+
+We provide a simple inference example in `eval/test_demo.py`. More evaluation scripts will be added soon.
+
+
 ## License Agreement
 
 PaDT is licensed under Apache 2.0.