Update README.md

README.md

@@ -7,7 +7,6 @@ size_categories:
 - 10K<n<100K
 task_categories:
 - object-detection
-- visual-question-answering
 tags:
 - computer-vision
 - diffusion-priors
@@ -21,67 +20,26 @@ configs:
       path: "ho_irany_test_rel_full.jsonl"
 ---
 
-# Dataset Card: Hidden-Objects
+# Hidden-Objects
 
-## 📌 Overview
-It provides image-object pairs with localized bounding boxes, designed to help models learn realistic object placement and spatial relationships within background scenes.
+Image-object pairs with localized bounding boxes for learning realistic object placement in background scenes.
 
-* **Project Page:** [https://hidden-objects.github.io/](https://hidden-objects.github.io/)
-* **Background Source:** [Places365 Dataset](http://places2.csail.mit.edu/download.html)
+- **Project page:** https://hidden-objects.github.io/
+- **Backgrounds:** [Places365](http://places2.csail.mit.edu/download.html)
 
-## 📊 Data Schema
-Each entry consists of a foreground object (`fg_class`) to be inserted within a background image (`bg_path`).
+## Schema
 
 | Field | Type | Description |
 |:---|:---|:---|
-| **entry_id** | `int64` | Unique identifier for the data row. |
-| **bg_path** | `string` | Relative file path to the background image in Places365. |
-| **fg_class** | `string` | Category name of the foreground object (e.g., "bottle"). |
-| **bbox** | `list` | Bounding box coordinates `[x, y, w, h]` (normalized 0–1). |
-| **label** | `int64` | 1 for positive annotation, 0 for negative. |
-| **image_reward_score** | `float64` | Ranker score from ImageReward. |
-| **confidence** | `float64` | Detection confidence score (GroundedDINO). |
+| `entry_id` | int64 | Unique row identifier |
+| `bg_path` | string | Relative path to background image in Places365 |
+| `fg_class` | string | Foreground object category (e.g. `"bottle"`) |
+| `bbox` | list | Normalized bounding box `[x, y, w, h]` in range 0–1 |
+| `label` | int64 | 1 = positive, 0 = negative |
+| `image_reward_score` | float64 | [ImageReward](https://github.com/THUDM/ImageReward) quality score |
+| `confidence` | float64 | GroundingDINO detection confidence |
+| `source` | string | Origin tag of the annotation |
 
----
-
-## 📐 Preprocessing & Bounding Boxes
-The bounding boxes are defined relative to a **512x512 center-cropped** version of the background image. 
-1. Resize the shortest side of the original image to **512px**.
-2. Perform a **center crop** to reach 512x512.
-3. The upper-left corner of the crop is `(0, 0)`.
-
-**Coordinate Conversion:**
-```python
-# Convert normalized [x, y, w, h] to 512x512 pixel coordinates
-px_x, px_y = bbox[0] * 512, bbox[1] * 512
-px_w, px_h = bbox[2] * 512, bbox[3] * 512
-```
-
-
-## Example Setup
-huggingface-cli login
-
-
-### Download Background Images from Places
-
-```python
-
-import torchvision.datasets as datasets
-
-root = "INSERT_YOUR_PATH"   
-dataset = datasets.Places365(root=root, split='train-standard', small=False, download=True)
-print(f"Downloaded {len(dataset)} images to {root}")
-```
-
-
-### Load as JSONL
-```Python
-from datasets import load_dataset
-
-dataset = load_dataset("marco-schouten/hidden-objects", streaming=True)
-first_row = next(iter(dataset["train"]))
-print(first_row)
-```
 Sample:
 ```json
 {
@@ -92,48 +50,44 @@ Sample:
   "label": 1,
   "image_reward_score": -1.542461,
   "confidence": 0.388181,
-  "source": "h"
+  "source": "ho"
 }
 ```
 
-### Load for Training / Evalauting
+## Bounding Boxes
 
-```Python
-import os
-import torch
-from PIL import Image
-from torch.utils.data import Dataset, DataLoader
+Bounding boxes are relative to a **512×512 center crop** of the background image:
+```python
+# Normalized → pixel coordinates
+x, y, w, h = [v * 512 for v in bbox]
+```
+
+## Usage
+
+### Quick start
+
+```python
 from datasets import load_dataset
-import torchvision.transforms as T
 
-class HiddenObjectsDataset(Dataset):
-    def __init__(self, places_root, split="train"):
-        self.hf_data = load_dataset("marco-schouten/hidden-objects", split=split)
-        self.places_root = places_root
-        self.transform = T.Compose([
-            T.Resize(512),
-            T.CenterCrop(512),
-            T.ToTensor()
-        ])
+dataset = load_dataset("marco-schouten/hidden-objects")
+print(dataset["train"][0])
+```
 
-    def __len__(self):
-        return len(self.hf_data)
+### PyTorch Dataset
 
-    def __getitem__(self, idx):
-        item = self.hf_data[idx]
-        img_path = os.path.join(self.places_root, item['bg_path'])
-        image = self.transform(Image.open(img_path).convert("RGB"))
-        bbox = torch.tensor(item['bbox']) * 512
-        return {"image": image, "bbox": bbox, "label": item['label'], "class": item['fg_class'], "image_reward_score" : item['image_reward_score']
-  "confidence" : item['confidence']}
+Requires Places365 backgrounds downloaded locally:
 
-# Usage
-# dataset = HiddenObjectsDataset(places_root="./data/places365")
+```bash
+huggingface-cli login
 ```
 
-### Load Streaming Mode
+```python
+import torchvision.datasets as datasets
 
-```Python
+background_images = datasets.Places365(root="./data/places365", split="train-standard", small=False, download=True)
+```
+
+```python
 import os
 import torch
 from PIL import Image
@@ -143,57 +97,26 @@ import torchvision.transforms as T
 
 class HiddenObjectsDataset(Dataset):
     def __init__(self, places_root, split="train"):
-        self.hf_data = load_dataset("marco-schouten/hidden-objects", split=split)
+        self.data = load_dataset("marco-schouten/hidden-objects", split=split)
         self.places_root = places_root
-        self.transform = T.Compose([
-            T.Resize(512),
-            T.CenterCrop(512),
-            T.ToTensor()
-        ])
+        self.transform = T.Compose([T.Resize(512), T.CenterCrop(512), T.ToTensor()])
 
     def __len__(self):
-        return len(self.hf_data)
+        return len(self.data)
 
     def __getitem__(self, idx):
-        item = self.hf_data[idx]
-        img_path = os.path.join(self.places_root, item['bg_path'])
-        image = self.transform(Image.open(img_path).convert("RGB"))
-        bbox = torch.tensor(item['bbox']) * 512
+        item = self.data[idx]
+        image = self.transform(Image.open(os.path.join(self.places_root, item["bg_path"])).convert("RGB"))
         return {
-            "entry_id": item['entry_id'],
-            "image": image, 
-            "bbox": bbox, 
-            "label": item['label'], 
-            "class": item['fg_class']
+            "entry_id": item["entry_id"],
+            "image": image,
+            "bbox": torch.tensor(item["bbox"]) * 512,
+            "label": item["label"],
+            "class": item["fg_class"],
+            "image_reward_score": item["image_reward_score"],
+            "confidence": item["confidence"],
         }
 
-### B. Streaming Loader (Best for Quick Start)
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-import torchvision.transforms as T
-import os
-from PIL import Image
-import torch
-
-def get_streaming_loader(places_root, batch_size=32):
-    dataset = load_dataset("marco-schouten/hidden-objects", split="train", streaming=True)
-    preprocess = T.Compose([T.Resize(512), T.CenterCrop(512), T.ToTensor()])
-
-    def collate_fn(batch):
-        images, bboxes, ids = [], [], []
-        for item in batch:
-            path = os.path.join(places_root, item['bg_path'])
-            try:
-                img = Image.open(path).convert("RGB")
-                images.append(preprocess(img))
-                bboxes.append(torch.tensor(item['bbox']) * 512)
-                ids.append(item['entry_id'])
-            except FileNotFoundError:
-                continue
-        return {
-            "entry_id": ids,
-            "pixel_values": torch.stack(images), 
-            "bboxes": torch.stack(bboxes)
-        }
-    return DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)
-```
+# Usage
+hidden_object_dataset = HiddenObjectsDataset(places_root="./data/places365")
+```