Update README.md

README.md

@@ -48,24 +48,136 @@ configs:
         path: Eardial_downstream_task_datasets/Image_captioning/RSICD_Captions/RSICD_Captions_test/data-00000-of-00001.arrow
 ---
 
-
 # 🌍 EarthDial-Dataset
 
-The **EarthDial-Dataset** is a collection of task-specific evaluation datasets curated for benchmarking remote sensing, satellite imagery, and Earth observation downstream tasks. It is designed to serve as a testbed for evaluating vision-language and multimodal models in real-world Earth monitoring use-cases.
+The **EarthDial-Dataset** is a curated collection of evaluation-only datasets focused on remote sensing and Earth observation downstream tasks. It is designed to benchmark **vision-language models (VLMs)** and **multimodal reasoning systems** on real-world scenarios involving satellite and aerial imagery.
+
+---
+
+## 📚 Key Features
+
+- **Evaluation-focused**: All datasets are for inference/testing only — no train/val splits.
+- **Diverse Tasks**:
+  - Classification
+  - Object Detection
+  - Change Detection
+  - Grounding Description
+  - Region Captioning
+  - Image Captioning
+  - Visual Question Answering (GeoChat Bench)
+- **Remote Sensing Specific**: Tailored for multispectral, RGB, and high-resolution satellite data.
+- **Multimodal Format**: Includes images, questions, captions, annotations, and geospatial metadata.
 
 ---
 
-## 📁 Dataset Structure
+## 🗂️ Dataset Structure
 
-This repository contains a main folder `EarthDial_downstream_task_datasets`, which includes several task-specific directories. Each directory contains multiple datasets in Apache Arrow format. There are **no training or validation splits** — these datasets are intended for **evaluation only**.
+The dataset is structured under the root folder:  
+`EarthDial_downstream_task_datasets/`
 
-## 📦 Sample Dataset Load
+Each task has its own subdirectory with `.arrow`-formatted shards, structured as:
 
+```bash
+EarthDial_downstream_task_datasets/
+│
+├── Classification/
+│   ├── AID/
+│   │   └── test/data-00000-of-00001.arrow
+│   └── ...
+│
+├── Detection/
+│   ├── NWPU_VHR_10_test/
+│   ├── Swimming_pool_dataset_test/
+│   └── ...
+│
+├── Region_captioning/
+│   └── NWPU_VHR_10_test_region_captioning/
+│
+├── Image_captioning/
+│   ├── RSICD_Captions/
+│   └── UCM_Captions/
+│...
 
+## 🗂️ Example data usage
 
-```python
 from datasets import load_dataset
 
 dataset = load_dataset(
     "akshaydudhane/EarthDial-Dataset",
-    data_dir="EarthDial_downstream_task_datasets/Classification/AID/test")
+    data_dir="EarthDial_downstream_task_datasets/Classification/AID/test"
+)
+
+## Example Demo Usage
+
+import argparse
+import torch
+from PIL import Image
+from transformers import AutoTokenizer
+from earthdial.model.internvl_chat import InternVLChatModel
+from earthdial.train.dataset import build_transform
+
+def run_single_inference(args):
+    # Load model and tokenizer from Hugging Face Hub
+    print(f"Loading model and tokenizer from Hugging Face: {args.checkpoint}")
+    tokenizer = AutoTokenizer.from_pretrained(args.checkpoint, trust_remote_code=True, use_fast=False)
+    model = InternVLChatModel.from_pretrained(
+        args.checkpoint,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.bfloat16,
+        device_map="auto" if args.auto else None,
+        load_in_8bit=args.load_in_8bit,
+        load_in_4bit=args.load_in_4bit
+    ).eval()
+
+    if not args.load_in_8bit and not args.load_in_4bit and not args.auto:
+        model = model.cuda()
+
+    # Load and preprocess image
+    image = Image.open(args.image_path).convert("RGB")
+    image_size = model.config.force_image_size or model.config.vision_config.image_size
+    transform = build_transform(is_train=False, input_size=image_size, normalize_type='imagenet')
+    pixel_values = transform(image).unsqueeze(0).cuda().to(torch.bfloat16)
+
+    # Generate answer
+    generation_config = {
+        "num_beams": args.num_beams,
+        "max_new_tokens": 100,
+        "min_new_tokens": 1,
+        "do_sample": args.temperature > 0,
+        "temperature": args.temperature,
+    }
+
+    answer = model.chat(
+        tokenizer=tokenizer,
+        pixel_values=pixel_values,
+        question=args.question,
+        generation_config=generation_config,
+        verbose=True
+    )
+
+    print("\n=== Inference Result ===")
+    print(f"Question: {args.question}")
+    print(f"Answer: {answer}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--checkpoint', type=str, required=True, help='Model repo ID on Hugging Face Hub')
+    parser.add_argument('--image-path', type=str, required=True, help='Path to local input image')
+    parser.add_argument('--question', type=str, required=True, help='Question to ask about the image')
+    parser.add_argument('--num-beams', type=int, default=5)
+    parser.add_argument('--temperature', type=float, default=0.0)
+    parser.add_argument('--load-in-8bit', action='store_true')
+    parser.add_argument('--load-in-4bit', action='store_true')
+    parser.add_argument('--auto', action='store_true')
+
+    args = parser.parse_args()
+    run_single_inference(args)
+
+
+
+python demo_infer.py \
+  --checkpoint akshaydudhane/EarthDial_4B_RGB \
+  --image-path ./test.jpg \
+  --question "Which road has more vehicles?" \
+  --auto
+