Complete dataset refresh with new version

README.md

@@ -1,13 +1,4 @@
----
-license: mit
-task_categories:
-- image-to-image
-language:
-- en
-pretty_name: a
-size_categories:
-- 10M<n<100M
----
+<!-- <h1 align="center">AnyInsertion dataset</h2> -->
 # AnyInsertion
 <p align="center">
 <a href="https://song-wensong.github.io/"><strong>Wensong Song</strong></a>
@@ -21,28 +12,23 @@ size_categories:
 <a href="https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=en"><strong>Yi Yang</strong></a>
 <br>
 <br>
-<a href="https://arxiv.org/pdf/2504.15009" style="display: inline-block; margin-right: 10px;">
-  <img src='https://img.shields.io/badge/arXiv-InsertAnything-red?color=%23aa1a1a' alt='Paper PDF'>
-</a>
-<a href='https://song-wensong.github.io/insert-anything/' style="display: inline-block; margin-right: 10px;">
-  <img src='https://img.shields.io/badge/Project%20Page-InsertAnything-cyan?logoColor=%23FFD21E&color=%23cbe6f2' alt='Project Page'>
-</a>
-<a href='https://github.com/song-wensong/insert-anything' style="display: inline-block;">
-  <img src='https://img.shields.io/badge/GitHub-InsertAnything-black?logoColor=23FFD21E&color=%231d2125'>
-</a>
+    <a href="https://arxiv.org/pdf/2504.15009"><img src='https://img.shields.io/badge/arXiv-InsertAnything-red?color=%23aa1a1a' alt='Paper PDF'></a>
+    <a href='https://song-wensong.github.io/insert-anything/'><img src='https://img.shields.io/badge/Project%20Page-InsertAnything-cyan?logoColor=%23FFD21E&color=%23cbe6f2' alt='Project Page'></a>
+    <a href=''><img src='https://img.shields.io/badge/Hugging%20Face-InsertAnything-yellow?logoColor=%23FFD21E&color=%23ffcc1c'></a>
 <br>
 <b>Zhejiang University &nbsp; | &nbsp; Harvard University &nbsp; | &nbsp;  Nanyang Technological University </b>
 </p>
 
 ## News
 
-* **[2025.4.25]** Released AnyInsertion v1 mask-prompt dataset on Hugging Face.
+* **[2025.5.7]** Release **AnyInsertion** v1 text prompt dataset on HuggingFace.
+* **[2025.4.24]** Release **AnyInsertion** v1 mask prompt dataset on HuggingFace.
 
 
 ## Summary
 This is the dataset proposed in our paper [**Insert Anything: Image Insertion via In-Context Editing in DiT**](https://arxiv.org/abs/2504.15009)
 
-AnyInsertion dataset consists of training and testing subsets. The training set includes 159,908 samples across two prompt types: 58,188 mask-prompt image pairs and 101,720 text-prompt image pairs;the test set includes 158 data pairs: 120 mask-prompt pairs and 38 text-prompt pairs.
+AnyInsertion dataset consists of training and testing subsets. The training set includes 136,385 samples across two prompt types: 58,188 mask-prompt image pairs and 78,197 text-prompt image pairs;the test set includes 158 data pairs: 120 mask-prompt pairs and 38 text-prompt pairs.
 
 AnyInsertion dataset covers diverse categories including human subjects, daily necessities, garments, furniture, and various objects.
 
@@ -57,43 +43,80 @@ AnyInsertion dataset covers diverse categories including human subjects, daily n
 
 
 data/
-├── train/
-│   ├── accessory/
-│   │   ├── ref_image/     # Reference image containing the element to be inserted
-│   │   ├── ref_mask/      # The mask corresponding to the inserted element
-│   │   ├── tar_image/     # Ground truth
-│   │   ├── tar_mask/      # The mask corresponding to the edited area of target image
-│   │
-│   ├── object/
-│   │   ├── ref_image/
-│   │   ├── ref_mask/
-│   │   ├── tar_image/
-│   │   ├── tar_mask/
-│   │
-│   └── person/
-│       ├── ref_image/
-│       ├── ref_mask/
-│       ├── tar_image/
-│       ├── tar_mask/
+├── text_prompt/
+│   ├── train/
+│   │   ├── accessory/
+│   │   │   ├── ref_image/      # Reference image containing the element to be inserted
+│   │   │   ├── ref_mask/       # The mask corresponding to the inserted element
+│   │   │   ├── tar_image/      # Ground truth
+│   │   │   └── src_image/      # Source images
+│   │   │       ├── add/        # Source image with the inserted element from Ground Truth removed
+│   │   │       └── replace/    # Source image where the inserted element in Ground Truth is replaced
+│   │   ├── object/
+│   │   │   ├── ref_image/      
+│   │   │   ├── ref_mask/      
+│   │   │   ├── tar_image/      
+│   │   │   └── src_image/      
+│   │   │       ├── add/       
+│   │   │       └── replace/    
+│   │   └── person/
+│   │       ├── ref_image/     
+│   │       ├── ref_mask/       
+│   │       ├── tar_image/     
+│   │       └── src_image/    
+│   │           ├── add/       
+│   │           └── replace/  
+│   └── test/
+│       ├── garment/
+│       │   ├── ref_image/      
+│       │   ├── ref_mask/      
+│       │   ├── tar_image/      
+│       │   └── src_image/      
+│       └── object/
+│           ├── ref_image/     
+│           ├── ref_mask/       
+│           ├── tar_image/     
+│           └── src_image/      
 │
-└── test/
-    ├── garment/
-    │   ├── ref_image/
-    │   ├── ref_mask/
-    │   ├── tar_image/
-    │   ├── tar_mask/
-    │
-    ├── object/
-    │   ├── ref_image/
-    │   ├── ref_mask/
-    │   ├── tar_image/
-    │   ├── tar_mask/
-    │
-    └── person/
-        ├── ref_image/
-        ├── ref_mask/
-        ├── tar_image/
-        ├── tar_mask/
+├── mask_prompt/
+│   ├── train/
+│   │   ├── accessory/
+│   │   │   ├── ref_image/     
+│   │   │   ├── ref_mask/      
+│   │   │   ├── tar_image/     
+│   │   │   ├── tar_mask/      # The mask corresponding to the edited area of target image
+│   │   ├── object/
+│   │   │   ├── ref_image/    
+│   │   │   ├── ref_mask/      
+│   │   │   ├── tar_image/     
+│   │   │   ├── tar_mask/      
+│   │   └── person/
+│   │       ├── ref_image/     
+│   │       ├── ref_mask/     
+│   │       ├── tar_image/    
+│   │       ├── tar_mask/   
+│   └── test/
+│       ├── garment/
+│       │   ├── ref_image/   
+│       │   ├── ref_mask/    
+│       │   ├── tar_image/    
+│       │   ├── tar_mask/    
+│       ├── object/
+│       │   ├── ref_image/    
+│       │   ├── ref_mask/    
+│       │   ├── tar_image/     
+│       │   ├── tar_mask/    
+│       └── person/
+│           ├── ref_image/   
+│           ├── ref_mask/   
+│           ├── tar_image/  
+│           ├── tar_mask/    
+
+    
+
+
+
+
 ```
 
 
@@ -117,152 +140,16 @@ data/
     <img src="examples/tar_mask.png" alt="Tar_mask" style="width: 100%;">
     <figcaption>Tar_mask</figcaption>
   </figure>
+  <figure style="margin: 10px; width: calc(25% - 20px);">
+    <img src="examples/add.png" alt="Add" style="width: 100%;">
+    <figcaption>Add</figcaption>
+  </figure>
+  <figure style="margin: 10px; width: calc(25% - 20px);">
+    <img src="examples/replace.png" alt="Replace" style="width: 100%;">
+    <figcaption>Replace</figcaption>
+  </figure>
 </div>
 
-## Usage
-This guide explains how to load and use the AnyInsertion dataset, specifically the subset focusing on mask-prompt image pairs, which has been prepared in Apache Arrow format for efficient loading with the Hugging Face `datasets` library.
-
-### Installation
-
-First, ensure you have the `datasets` library installed. If not, you can install it via pip:
-
-```bash
-pip install datasets pillow
-```
-
-### Loading the Dataset
-You can load the dataset directly from the Hugging Face Hub using its identifier:
-
-```python
-from datasets import load_dataset
-
-# Replace with the correct Hugging Face Hub repository ID
-repo_id = "WensongSong/AnyInsertion"
-
-# Load the entire dataset (usually returns a DatasetDict with 'train' and 'test' splits)
-dataset = load_dataset(repo_id)
-
-print(dataset)
-# Expected output similar to:
-# DatasetDict({
-#     train: Dataset({
-#         features: ['id', 'split', 'category', 'main_label', 'ref_image', 'ref_mask', 'tar_image', 'tar_mask'],
-#         num_rows: XXXX
-#     })
-#     test: Dataset({
-#         features: ['id', 'split', 'category', 'main_label', 'ref_image', 'ref_mask', 'tar_image', 'tar_mask'],
-#         num_rows: YYYY
-#     })
-# })
-```
-
-### Loading Specific Splits
-If you only need a specific split (e.g., 'test'), you can specify it during loading:
-``` python
-# Load only the 'test' split
-test_dataset = load_dataset(repo_id, split='test')
-print("Loaded Test Split:")
-print(test_dataset)
-
-# Load only the 'train' split
-train_dataset = load_dataset(repo_id, split='train')
-print("\nLoaded Train Split:")
-print(train_dataset)
-```
-
-### Dataset Structure
-* The loaded dataset (or individual splits) has the following structure and features (columns):
-
-* id (string): A unique identifier for each data sample, typically formatted as "split/category/image_id" (e.g., "train/accessory/0").
-
-* split (string): Indicates whether the sample belongs to the 'train' or 'test' set.
-
-* category (string): The category of the main object or subject in the sample. Possible values include: 'accessory', 'object', 'person' (for train), 'garment', 'object_test', 'person' (for test).
-
-* main_label (string): The label associated with the reference image/mask pair, derived from the original label.json files.
-
-* ref_image (Image): The reference image containing the object or element to be conceptually inserted. Loaded as a PIL (Pillow) Image object.
-
-* ref_mask (Image): The binary mask highlighting the specific element within the ref_image. Loaded as a PIL Image object.
-
-* tar_image (Image): The target image, representing the ground truth result after the conceptual insertion or editing. Loaded as a PIL Image object.
-
-* tar_mask (Image): The binary mask indicating the edited or inserted region within the tar_image. Loaded as a PIL Image object.
-
-### Accessing Data
-
-You can access data like a standard Python dictionary or list:
-
-```python
-# Get the training split from the loaded DatasetDict
-train_ds = dataset['train']
-
-# Get the first sample from the training set
-first_sample = train_ds[0]
-
-# Access specific features (columns) of the sample
-ref_image = first_sample['ref_image']
-label = first_sample['main_label']
-category = first_sample['category']
-
-print(f"\nFirst train sample category: {category}, label: {label}")
-print(f"Reference image size: {ref_image.size}") # ref_image is a PIL Image
-
-# Display the image (requires matplotlib or other image libraries)
-# import matplotlib.pyplot as plt
-# plt.imshow(ref_image)
-# plt.title(f"Category: {category}, Label: {label}")
-# plt.show()
-
-# Iterate through the dataset (e.g., the first 5 test samples)
-print("\nIterating through the first 5 test samples:")
-test_ds = dataset['test']
-for i in range(5):
-    sample = test_ds[i]
-    print(f"  Sample {i}: ID={sample['id']}, Category={sample['category']}, Label={sample['main_label']}")
-```
-
-### Filtering Data
-
-The datasets library provides powerful filtering capabilities.
-
-```python
-# Filter the training set to get only 'accessory' samples
-accessory_train_ds = train_ds.filter(lambda example: example['category'] == 'accessory')
-print(f"\nNumber of 'accessory' samples in train split: {len(accessory_train_ds)}")
-
-# Filter the test set for 'person' samples
-person_test_ds = test_ds.filter(lambda example: example['category'] == 'person')
-print(f"Number of 'person' samples in test split: {len(person_test_ds)}")
-```
-#### Filtering by Split (if loaded as DatasetDict)
-Although loading specific splits is preferred, you can also filter by the split column if you loaded the entire DatasetDict and somehow combined them (not typical, but possible):
-
-```python
-# Assuming 'combined_ds' is a dataset containing both train and test rows
-# test_split_filtered = combined_ds.filter(lambda example: example['split'] == 'test')
-```
-
-### Working with Images
-The features defined as Image (ref_image, ref_mask, tar_image, tar_mask) will automatically load the image data as PIL (Pillow) Image objects when accessed. You can then use standard Pillow methods or convert them to other formats (like NumPy arrays or PyTorch tensors) for further processing.
-
-```python
-# Example: Convert reference image to NumPy array
-import numpy as np
-
-first_sample = train_ds[0]
-ref_image_pil = first_sample['ref_image']
-ref_image_np = np.array(ref_image_pil)
-
-print(f"\nReference image shape as NumPy array: {ref_image_np.shape}")
-```
-
-## Citation
-```
-@article{song2025insert,
-  title={Insert Anything: Image Insertion via In-Context Editing in DiT},
-  author={Song, Wensong and Jiang, Hong and Yang, Zongxing and Quan, Ruijie and Yang, Yi},
-  journal={arXiv preprint arXiv:2504.15009},
-  year={2025}
-}
-```
+### Text Prompt
+Add Prompt: Add [label from `tar_image` (in label.json) ]</p>
+Replace Prompt: Replace [label from `src_image` (in src_image/replace/replace_label.json) ] with [label from `tar_image` (in label.json) ]

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