Update README.md

README.md

@@ -5,24 +5,22 @@ task_categories:
 language:
 - en
 size_categories:
-- 1K<n<100K
+- 1K<n<10K
 ---
 
 # Dataset Card: Taiwan Wildlife Imagery Dataset
 
 ## Dataset Summary
-This dataset is primarily designed for training and evaluating Taiwan wildlife image classification models, with a specific focus on Continual Learning setup. The total storage size is approximately 5GB, comprising wildlife monitoring imagery captured via infrared trigger cameras across various altitudinal gradients in Taiwan.
+This dataset is primarily designed for training and evaluating Taiwan wildlife image classification models. The total storage size is approximately 5GB, comprising wildlife monitoring imagery captured via infrared trigger cameras across various altitudinal gradients in Taiwan.
 
-The dataset focuses on medium-to-large terrestrial mammals (e.g., Formosan Reeves's muntjac, Formosan macaque) and ground-dwelling birds. To comply with the Double-blind Review principles and information security protocols, all precise geographical coordinates have been generalized, and any accidental human captures, patrol records, or proprietary equipment identifiers have been strictly filtered out to ensure complete privacy and metadata anonymity. This dataset operates independently and has no association with any specific school, academic institution, or commercial enterprise.
+The dataset focuses on medium-to-large terrestrial mammals (e.g., Reeves's Muntjac, Formosan Macaque) and ground-dwelling birds. To fully comply with information security protocols and Double-blind Review principles, all precise geographical coordinates have been generalized. Furthermore, any accidental human captures, patrol footprints, or proprietary equipment identifiers have been strictly filtered out to ensure complete privacy and metadata anonymity. This dataset operates independently and has no association or data linkage with any specific school, academic institution, or commercial enterprise.
 
 ## Dataset Structure
 
 ### File Structure
-The dataset is structured into two main training dimensions (Geographical Region and Taxonomic Splits) to facilitate the evaluation of incremental or continual learning algorithms:
-
 ```text
 .
-├── train_by_region/    # Training set: Sub-datasets split by Geographical Region for continual learning.
+├── train/         # Training set partitioned by Geographical Region, suitable for continual learning.
   ├──set1(Taipei, New Taipei, Keelung)
     ├──00(Cat)
     ├──01(Civet)
@@ -55,7 +53,7 @@ The dataset is structured into two main training dimensions (Geographical Region
     .
     └──17(Urva)
 
-├── train_by_taxonomic/ # Training set: Sub-datasets split by Taxonomic categories for continual learning.
+├── train/         # Training set partitioned by Taxonomic categories, suitable for continual learning.
   ├──set1(Carnivora)
     ├──00(Cat)
     ├──01(Civet)
@@ -79,7 +77,7 @@ The dataset is structured into two main training dimensions (Geographical Region
   └──set6(Primates)
     └──06(Formosan Macaque)
 
-└── test/               # Independent test set (containing all 17 categories)
+└── test/          # Independent test set
   ├──00(Cat)
   ├──01(Civet)
   ├──02(Dog)
@@ -92,20 +90,21 @@ The dataset is structured into two main training dimensions (Geographical Region
 ## Data Collection & Preprocessing
 
 ### Data Sources
-* This dataset was legally collected by an independent research group through field surveys or integrated and provided by anonymous ecological volunteers under strict de-identification conditions.
+* This dataset was legally collected by an independent research group through field operations or integrated and provided by anonymous ecological volunteers under strict de-identification conditions.
 * **Disclaimer**: This dataset does not sync, link, or associate with the internal institutional data of any specific university, academic department, or commercial entity.
 
 ### Data Anonymization
-To maintain information security, data privacy, and academic double-blindness, the following de-identification procedures have been rigorously executed:
+To maintain rigorous information security, data privacy, and academic double-blindness, the following de-identification procedures have been fully executed:
 1. **Direct Identifier Removal**: Personal information such as names, student IDs, national ID numbers, emails, phone numbers, and IP addresses have been completely removed.
 2. **Sensitive Text Redaction**: Any text mentioning specific "University Names", "Department/Laboratory Names", "Advisor Names", or "Specific Experimental Forest Stations" has been entirely redacted and replaced with generic placeholders such as `[REDACTED]` or `[UNIVERSITY]`.
-3. **Metadata Cleaning**: All uploaded image files (.jpg/.png) have been processed with automated scripts to strip out EXIF metadata (including camera serial numbers, precise GPS coordinates, desktop computer usernames, and software license information) to prevent reverse tracking via system records.
+3. **Metadata Cleaning**: All uploaded image files have been processed with automated tools to strip out EXIF metadata (including camera serial numbers, precise GPS coordinates, desktop computer usernames, and software license information) to prevent reverse tracking via system records.
 
 ## Limitations & License
 * This dataset is distributed under the [MIT License].
 * Users of this dataset must strictly adhere to personal privacy and academic ethics principles. **It is strictly prohibited to maliciously cross-reference image backgrounds, residual data patterns, or cross-regional clues to reverse-engineer or identify specific individuals, research groups, or academic institutions.** Users shall bear full academic and legal liabilities for any such improper usage.
 
 
+
 ---
 license: mit
 task_categories:
@@ -120,7 +119,7 @@ size_categories:
 
 ## 資料集概覽 (Dataset Summary)
 本資料集主要用於臺灣野生動物影像分類任務訓練。資料總容量約為 5GB，包含本資料集包含 5GB 的野生動物監測影像，由紅外線觸發相機在台灣不同海拔梯度區域拍攝。
-資料集重點關注中大型陸棲哺乳動物（如台灣山羌、台灣獼猴）及地棲鳥類。所有地理座標均已進行概括處理，且已嚴格剔除意外拍攝到的人員獼猴）及地棲鳥類。
+資料集重點關注中大型陸棲哺乳動物（如台灣山羌、台灣獼猴）及地棲鳥類。所有地理座標均已進行概括處理，且已嚴格剔除意外拍攝到的人員及地棲鳥類。
 所有地理座標均已進行概括處理，且已嚴格剔除意外拍攝的人員影像及專有設備的隱私性。
 
 本資料集在發布與託管前，已進行嚴格的去識別化與隱私審查，確保不包含任何個人隱私、學校機關或特定組織之識別資訊。