Datasets:

AI-EcoNet
/

HUGO-Bench

@@ -1,230 +1,197 @@
-# ViT Animal Dataset
-This dataset contains 139,111 cropped images of 60+ animal species organized by taxonomic class (Aves and Mammals), optimized for streaming and efficient access.
-## Dataset Structure
-The dataset is stored in Parquet format to enable:
-- **Streaming**: Load data without downloading the entire dataset
-- **Efficient updates**: Update metadata without re-uploading images
-- **Fast access**: Optimized columnar storage
-### Splits
-- `aves`: 73,528 images of 30 bird species
-- `mammals`: 65,583 images of 30 mammal species
-### Columns
-- `image`: PIL Image object
-- `label`: Taxonomic class (Aves or Mammals)
-- `filename`: Original filename with species identifier (e.g., "american-crow_0001.jpg")
-### Species Organization
-Filenames follow the pattern: `{species-name}_{number}.jpg` or `uncertain_{species-name}_{number}.jpg`
-- Species names use lowercase with hyphens (e.g., "american-crow", "red-fox")
-- "uncertain_" prefix indicates cases where manual validation was uncertain of the given class
-## Usage
-### Streaming (recommended for large datasets)
-```python
-from datasets import load_dataset
-# Load with streaming enabled
-dataset = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
-# Iterate through samples
-for sample in dataset:
-    image = sample['image']
-    label = sample['label']
-    filename = sample['filename']
-    # Process your data
-```
-### Filter by species (streaming)
-```python
-from datasets import load_dataset
-# Stream and filter for specific species
-ds = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
-crows_only = ds.filter(lambda x: x['filename'].startswith('american-crow'))
-for sample in crows_only:
-    print(sample['filename'])
-```
-### Load specific subset
-```python
-from datasets import load_dataset
-# Load only first 1000 images from aves
-dataset = load_dataset("AI-EcoNet/ViT_animal_data", split="aves[:1000]")
-# Load 10% of mammals
-dataset = load_dataset("AI-EcoNet/ViT_animal_data", split="mammals[:10%]")
-```
-### Load exactly 200 images per species (validated only)
-```python
-from datasets import load_dataset
-from collections import defaultdict
-# Load with streaming
-ds = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
-# Collect 200 validated images per species
-species_list = ['american-crow', 'australian-magpie', 'kea']
-species_counts = defaultdict(int)
-species_samples = defaultdict(list)
-for sample in ds:
-    filename = sample['filename']
-    # Skip uncertain images
-    if filename.startswith('uncertain_'):
-        continue
-    # Extract species name
-    species = filename.rsplit('_', 1)[0]
-    # Collect if we need more
-    if species in species_list and species_counts[species] < 200:
-        species_samples[species].append(sample)
-        species_counts[species] += 1
-        # Stop when we have 200 of each
-        if all(species_counts[s] >= 200 for s in species_list):
-            break
-print(f"Collected: {dict(species_counts)}")
-# Output: {'american-crow': 200, 'australian-magpie': 200, 'kea': 200}
-```
-### Full download
-```python
-from datasets import load_dataset
-# Download full dataset
-dataset = load_dataset("AI-EcoNet/ViT_animal_data")
-# Access by split
-aves_data = dataset['aves']
-mammals_data = dataset['mammals']
-```
-## Dataset Details
-- **Format**: Parquet (optimized for streaming)
-- **Total Images**: 139,111
-- **Species Count**: 61 species (31 birds, 30 mammals)
-- **Image Format**: RGB images, variable sizes (cropped from original images)
-- **License**: MIT
-- **Source**: Images from 23 camera trap datasets from LILA BC
-### Complete Species List
-#### Aves (Birds) - 31 Species, 73,528 Images
-| Species | Total | Validated | Uncertain |
-|---------|-------|-----------|-----------|
-| American crow | 1,240 | 1,234 | 6 |
-| Australasian swamphen | 2,772 | 2,772 | 0 |
-| Australian magpie | 2,739 | 2,733 | 6 |
-| Black curassow | 1,949 | 1,939 | 10 |
-| Blue whistling thrush | 2,157 | 2,128 | 29 |
-| Brown quail | 2,662 | 2,652 | 10 |
-| Chicken | 1,647 | 1,629 | 18 |
-| Common chafflinch | 2,384 | 2,370 | 14 |
-| Common myna | 2,707 | 2,704 | 3 |
-| Dunnock | 2,066 | 2,055 | 11 |
-| European starling | 1,876 | 1,867 | 9 |
-| Fantails | 2,127 | 2,121 | 6 |
-| Greenfinch | 2,503 | 2,483 | 20 |
-| Kea | 2,833 | 2,814 | 19 |
-| Kiwi | 1,634 | 1,620 | 14 |
-| Kori bustard | 1,485 | 1,484 | 1 |
-| Mountain quail | 2,337 | 2,337 | 0 |
-| New Zealand robin | 2,753 | 2,719 | 34 |
-| Ostrich | 3,727 | 3,722 | 5 |
-| Petrel | 2,370 | 2,354 | 16 |
-| Pipit | 2,316 | 2,238 | 78 |
-| Quail | 44 | 0 | 44 |
-| Red junglefowl | 2,684 | 2,671 | 13 |
-| Spix's guan | 3,032 | 3,022 | 10 |
-| Swamp harrier | 2,917 | 2,917 | 0 |
-| Takahē | 2,085 | 2,073 | 12 |
-| Tūī | 2,767 | 2,747 | 20 |
-| Vulturine guineafowl | 4,420 | 4,404 | 16 |
-| Weka | 2,421 | 2,407 | 14 |
-| Wild turkey | 2,057 | 2,057 | 0 |
-| Yellow-eyed penguin | 2,817 | 2,815 | 2 |
-#### Mammals - 30 Species, 65,583 Images
-| Species | Total | Validated | Uncertain |
-|---------|-------|-----------|-----------|
-| Alpaca | 1,864 | 1,864 | 0 |
-| American black bear | 2,912 | 2,797 | 115 |
-| Black-backed jackal | 2,067 | 2,053 | 14 |
-| Black rhinoceros | 1,428 | 1,419 | 9 |
-| Bushpig | 1,241 | 1,230 | 11 |
-| Common brushtail possum | 1,365 | 1,358 | 7 |
-| Crab-eating mongoose | 2,364 | 2,316 | 48 |
-| Crested porcupine | 1,449 | 1,449 | 0 |
-| Dromedary camel | 1,809 | 1,798 | 11 |
-| Eastern gray squirrel | 2,434 | 2,414 | 20 |
-| Ferret badger | 1,743 | 1,671 | 72 |
-| Gemsbok | 3,963 | 3,944 | 19 |
-| Giant armadillo | 463 | 461 | 2 |
-| Giraffe | 2,985 | 2,974 | 11 |
-| Greater kudu | 4,410 | 4,375 | 35 |
-| Hippopotamus | 2,319 | 2,314 | 5 |
-| Jaguar | 6,855 | 6,831 | 24 |
-| L'Hoest's monkey | 2,941 | 2,921 | 20 |
-| Least weasel | 1,869 | 1,869 | 0 |
-| Northern treeshrew | 699 | 687 | 12 |
-| NZ sea lion | 2,296 | 2,277 | 19 |
-| Raccoon | 2,700 | 2,577 | 123 |
-| Serval | 625 | 625 | 0 |
-| Ship rat | 822 | 822 | 0 |
-| Spotted paca | 1,715 | 1,673 | 42 |
-| Stump-tailed macaque | 3,558 | 3,535 | 23 |
-| Sun bear | 713 | 709 | 4 |
-| Warthog | 1,671 | 1,666 | 5 |
-| White-nosed coati | 1,996 | 1,982 | 14 |
-| Wolf | 2,307 | 2,307 | 0 |
-**Note**: "Uncertain" indicates cases where manual validation was uncertain of the given class due to occlusion, blur, or ambiguous features.
-## Downloading Specific Species
-**Note**: The current Parquet structure contains mixed species in each file. To download only specific species efficiently, use streaming mode with filtering:
-```python
-import pandas as pd
-from datasets import load_dataset
-# Option 1: Use streaming with filter
-ds = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
-filtered = ds.filter(lambda x: x['filename'].startswith('american-crow'))
-# Option 2: Use lookup table (download from dataset repository)
-# The lookup table allows you to identify which samples to load
-# without downloading the full dataset
-```
-## Citation
-If you use this dataset, please cite appropriately. Each species folder contains the original image source and datasets from LILA BC.
-```bibtex
-@dataset{markoff2025vit_animal_dataset,
-  title={ViT Animal Dataset: Camera Trap Images for Vision Transformer Evaluation},
-  author={Markoff, Hugo and Galaktionovs, Jurijs},
-  year={2025},
-  publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/datasets/AI-EcoNet/ViT_animal_data}}
-}
-```
-## Acknowledgments
-This dataset aggregates cropped images from 23 camera trap datasets available through LILA BC (Labeled Information Library of Alexandria: Biology and Conservation). We thank all the organizations and researchers who contributed to the original camera trap projects.

+# ViT Animal Dataset
+This dataset contains 139,111 cropped images of 60 animal species organized Aves and Mammalia.
+The raw image data was derived from 23 different projects across LILA BC, filtered and cropped to only include the detected species.
+The data has been used to benchmark different ViT models, combined with dimension reduction & clustering methods.
+## Dataset Details
+- **Format**: Parquet (optimized for streaming)
+- **Total Images**: 139,111
+- **Species Count**: 60 species (30 birds, 30 mammals)
+- **License**: MIT
+### Splits
+- `aves`: 73,528 images of 30 bird species
+- `mammals`: 65,583 images of 30 mammal species
+### Columns
+- `image`: PIL Image object
+- `label`: Taxonomic class (Aves or Mammals)
+- `filename`: Original filename with species identifier (e.g., "american-crow_0001.jpg")
+### Species Organization
+Filenames follow the pattern: `{species-name}_{number}.jpg` or `uncertain_{species-name}_{number}.jpg`
+- Species names use lowercase with hyphens (e.g., "american-crow", "red-fox")
+- "uncertain_" prefix indicates cases where manual validation was uncertain of the given class
+## Usage
+### Streaming (recommended for large datasets)
+```python
+from datasets import load_dataset
+# Load with streaming enabled
+dataset = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
+# Iterate through samples
+for sample in dataset:
+    image = sample['image']
+    label = sample['label']
+    filename = sample['filename']
+    # Process your data
+```
+### Filter by species (streaming)
+```python
+from datasets import load_dataset
+# Stream and filter for specific species
+ds = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
+crows_only = ds.filter(lambda x: x['filename'].startswith('american-crow'))
+for sample in crows_only:
+    print(sample['filename'])
+```
+### Load specific subset
+```python
+from datasets import load_dataset
+# Load only first 1000 images from aves
+dataset = load_dataset("AI-EcoNet/ViT_animal_data", split="aves[:1000]")
+# Load 10% of mammals
+dataset = load_dataset("AI-EcoNet/ViT_animal_data", split="mammals[:10%]")
+```
+### Load exactly 200 images per species (validated only)
+```python
+from datasets import load_dataset
+from collections import defaultdict
+# Load with streaming
+ds = load_dataset("AI-EcoNet/ViT_animal_data", split="aves", streaming=True)
+# Collect 200 validated images per species
+species_list = ['american-crow', 'australian-magpie', 'kea']
+species_counts = defaultdict(int)
+species_samples = defaultdict(list)
+for sample in ds:
+    filename = sample['filename']
+    # Skip uncertain images
+    if filename.startswith('uncertain_'):
+        continue
+    # Extract species name
+    species = filename.rsplit('_', 1)[0]
+    # Collect if we need more
+    if species in species_list and species_counts[species] < 200:
+        species_samples[species].append(sample)
+        species_counts[species] += 1
+        # Stop when we have 200 of each
+        if all(species_counts[s] >= 200 for s in species_list):
+            break
+print(f"Collected: {dict(species_counts)}")
+# Output: {'american-crow': 200, 'australian-magpie': 200, 'kea': 200}
+```
+### Full download
+```python
+from datasets import load_dataset
+# Download full dataset
+dataset = load_dataset("AI-EcoNet/ViT_animal_data")
+# Access by split
+aves_data = dataset['aves']
+mammals_data = dataset['mammals']
+```
+### Complete Species List
+#### Aves (Birds) - 30 Species, 73,528 Images
+| Species | Total | Validated | Uncertain |
+|---------|-------|-----------|-----------|
+| American crow | 1,240 | 1,234 | 6 |
+| Australasian swamphen | 2,772 | 2,772 | 0 |
+| Australian magpie | 2,739 | 2,733 | 6 |
+| Black curassow | 1,949 | 1,939 | 10 |
+| Blue whistling thrush | 2,157 | 2,128 | 29 |
+| Brown quail | 2,662 | 2,652 | 10 |
+| Chicken | 1,647 | 1,629 | 18 |
+| Common chafflinch | 2,384 | 2,370 | 14 |
+| Common myna | 2,707 | 2,704 | 3 |
+| Dunnock | 2,066 | 2,055 | 11 |
+| European starling | 1,876 | 1,867 | 9 |
+| Fantails | 2,127 | 2,121 | 6 |
+| Greenfinch | 2,503 | 2,483 | 20 |
+| Kea | 2,833 | 2,814 | 19 |
+| Kiwi | 1,634 | 1,620 | 14 |
+| Kori bustard | 1,485 | 1,484 | 1 |
+| Mountain quail | 2,381 | 2,337 | 44 |
+| New Zealand robin | 2,753 | 2,719 | 34 |
+| Ostrich | 3,727 | 3,722 | 5 |
+| Petrel | 2,370 | 2,354 | 16 |
+| Pipit | 2,316 | 2,238 | 78 |
+| Quail | 44 | 0 | 44 |
+| Red junglefowl | 2,684 | 2,671 | 13 |
+| Spix's guan | 3,032 | 3,022 | 10 |
+| Swamp harrier | 2,917 | 2,917 | 0 |
+| Takahē | 2,085 | 2,073 | 12 |
+| Tūī | 2,767 | 2,747 | 20 |
+| Vulturine guineafowl | 4,420 | 4,404 | 16 |
+| Weka | 2,421 | 2,407 | 14 |
+| Wild turkey | 2,057 | 2,057 | 0 |
+| Yellow-eyed penguin | 2,817 | 2,815 | 2 |
+#### Mammals - 30 Species, 65,583 Images
+| Species | Total | Validated | Uncertain |
+|---------|-------|-----------|-----------|
+| Alpaca | 1,864 | 1,864 | 0 |
+| American black bear | 2,912 | 2,797 | 115 |
+| Black-backed jackal | 2,067 | 2,053 | 14 |
+| Black rhinoceros | 1,428 | 1,419 | 9 |
+| Bushpig | 1,241 | 1,230 | 11 |
+| Common brushtail possum | 1,365 | 1,358 | 7 |
+| Crab-eating mongoose | 2,364 | 2,316 | 48 |
+| Crested porcupine | 1,449 | 1,449 | 0 |
+| Dromedary camel | 1,809 | 1,798 | 11 |
+| Eastern gray squirrel | 2,434 | 2,414 | 20 |
+| Ferret badger | 1,743 | 1,671 | 72 |
+| Gemsbok | 3,963 | 3,944 | 19 |
+| Giant armadillo | 463 | 461 | 2 |
+| Giraffe | 2,985 | 2,974 | 11 |
+| Greater kudu | 4,410 | 4,375 | 35 |
+| Hippopotamus | 2,319 | 2,314 | 5 |
+| Jaguar | 6,855 | 6,831 | 24 |
+| L'Hoest's monkey | 2,941 | 2,921 | 20 |
+| Least weasel | 1,869 | 1,869 | 0 |
+| Northern treeshrew | 699 | 687 | 12 |
+| NZ sea lion | 2,296 | 2,277 | 19 |
+| Raccoon | 2,700 | 2,577 | 123 |
+| Serval | 625 | 625 | 0 |
+| Ship rat | 822 | 822 | 0 |
+| Spotted paca | 1,715 | 1,673 | 42 |
+| Stump-tailed macaque | 3,558 | 3,535 | 23 |
+| Sun bear | 713 | 709 | 4 |
+| Warthog | 1,671 | 1,666 | 5 |
+| White-nosed coati | 1,996 | 1,982 | 14 |
+| Wolf | 2,307 | 2,307 | 0 |
+**Note**: "Uncertain" indicates cases where manual validation was uncertain of the intitial label.
+## Citation
+If you use this dataset, please cite appropriately. Each species folder contains the original image source and datasets from LILA BC, which can be cited seperately.
+```bibtex
+@dataset{TBA
+}
+```