Update catalog metadata for tropical Africa tiles (west/central/east-africa)

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	---
	pretty_name: Perch v2 Models (full + regional catalog)
	license: apache-2.0
	base_model:
	- cgeorgiaw/Perch
	tags:
	- bioacoustics
	- birdnet
	- perch
	- bird
	- vocalization
	- audio-classification
	- onnx
	library_name: onnx
	---

	# Perch v2 Models (full + regional catalog)

	Google's Perch v2 bioacoustic classifier in three deployment variants, plus a catalog of region-specific slices that are smaller and faster while staying numerically identical (bit-exact) to the full model on the species they keep.

	## Origin and attribution

	- Perch v2 by Google Research ([bird-vocalization-classifier](https://www.kaggle.com/models/google/bird-vocalization-classifier/)): EfficientNet-B3, ~12M embedding + ~91M classification params, ~15,000 species.
	- ONNX conversion and the DFT-to-MatMul (`no_dft`) optimization by [justinchuby](https://huggingface.co/justinchuby/Perch-onnx).
	- Labels from [cgeorgiaw/Perch](https://huggingface.co/cgeorgiaw/Perch) (iNaturalist taxonomy).
	- Regional slicing uses the BirdNET Geomodel v3.0 range filter (birdnet-team/geomodel) to pick each region's species.

	## Variants and hardware

	\| filename token \| precision \| best for \|
	\|---\|---\|---\|
	\| `_fp32` \| FP32, with DFT \| GPU (CUDA/TensorRT), Intel CPU \|
	\| `_no_dft_fp32` \| FP32, DFT removed \| OpenVINO (RPi5 fast path); also runs on ORT/CUDA \|
	\| `_int8_arm` \| partial INT8 (MatMul-only) \| ARM CPU / Raspberry Pi, low RAM \|

	## Full model

	\| file \| MB \|
	\|---\|---:\|
	\| `full/perch_v2_fp32.onnx` \| 409 \|
	\| `full/perch_v2_no_dft_fp32.onnx` \| 413 \|
	\| `full/perch_v2_int8_arm.onnx` \| 131 \|
	\| `full/perch_v2_labels.txt` \| 14,795 classes \|

	## Why regional models

	These slices are built for real-time detection on resource-constrained devices, phones, Raspberry Pi and other single-board computers, where running the full 14,795-class Perch v2 continuously is costly in both RAM and CPU. Most of that cost goes to recognising species that cannot occur at the listener's location. Restricting the model to a region's species shrinks the memory footprint and the per-inference compute (the classifier head is ~88% of the model), so an always-on detector keeps up with the live audio stream and leaves headroom for the rest of the application, on hardware where the full model would struggle. Each tile stays bit-exact to the full model on the species it keeps; the only change is that out-of-region species are not emitted, which at a fixed monitoring location is exactly what you want.

	## Regional catalog

	Each tile: BirdNET Geomodel v3.0 range filter (top ~800 species for temperate regions, ~1200 for bird-rich tropical/subtropical ones, up to ~3500 for the hyper-diverse Neotropics) + 198 FSD50K sound events + a 27-species cosmopolitan core. Ships `_no_dft_fp32` (OpenVINO/GPU) and `_int8_arm` (ARM) + labels + indices. All bit-exact vs the full model on the species they keep.

	Each tile folder also has `coverage.png` (a map of the region it covers) and `metadata.json` (species count, covered countries, and the continental `group` it belongs to).

	Tiles are organised by continent below. `regional/groups.json` lists the same grouping (ordered continents -> tiles) in one file, and each tile's `metadata.json` carries its `group` / `group_display` / `group_order`, so an application can rebuild these sections without scraping this table.

	### Europe

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `nordic` \| <img src="regional/nordic/coverage.png" width="210"> \| 638 \| 65.0 \| 44.5 \|
	\| `british-isles` \| <img src="regional/british-isles/coverage.png" width="210"> \| 776 \| 68.4 \| 45.4 \|
	\| `central-europe` \| <img src="regional/central-europe/coverage.png" width="210"> \| 873 \| 70.8 \| 46.0 \|
	\| `baltics` \| <img src="regional/baltics/coverage.png" width="210"> \| 655 \| 65.4 \| 44.6 \|
	\| `iberia` \| <img src="regional/iberia/coverage.png" width="210"> \| 856 \| 70.4 \| 45.9 \|
	\| `southern-europe` \| <img src="regional/southern-europe/coverage.png" width="210"> \| 839 \| 70.0 \| 45.8 \|
	\| `eastern-europe` \| <img src="regional/eastern-europe/coverage.png" width="210"> \| 739 \| 67.5 \| 45.2 \|
	\| `western-palearctic` \| <img src="regional/western-palearctic/coverage.png" width="210"> \| 1599 \| 88.7 \| 50.5 \|
	\| `iceland` \| <img src="regional/iceland/coverage.png" width="210"> \| 591 \| 63.9 \| 44.3 \|
	\| `svalbard` \| <img src="regional/svalbard/coverage.png" width="210"> \| 480 \| 61.1 \| 43.6 \|
	\| `canary-islands` \| <img src="regional/canary-islands/coverage.png" width="210"> \| 598 \| 64.0 \| 44.3 \|
	\| `madeira` \| <img src="regional/madeira/coverage.png" width="210"> \| 459 \| 60.6 \| 43.4 \|
	\| `azores` \| <img src="regional/azores/coverage.png" width="210"> \| 426 \| 59.8 \| 43.2 \|

	### Asia

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `south-asia-peninsular` \| <img src="regional/south-asia-peninsular/coverage.png" width="210"> \| 879 \| 70.9 \| 46.0 \|
	\| `indo-gangetic` \| <img src="regional/indo-gangetic/coverage.png" width="210"> \| 1406 \| 83.9 \| 49.3 \|
	\| `himalaya` \| <img src="regional/himalaya/coverage.png" width="210"> \| 1407 \| 83.9 \| 49.3 \|
	\| `japan` \| <img src="regional/japan/coverage.png" width="210"> \| 799 \| 69.0 \| 45.5 \|
	\| `china-northeast` \| <img src="regional/china-northeast/coverage.png" width="210"> \| 877 \| 70.9 \| 46.0 \|
	\| `china-north-central` \| <img src="regional/china-north-central/coverage.png" width="210"> \| 976 \| 73.3 \| 46.6 \|
	\| `china-southeast` \| <img src="regional/china-southeast/coverage.png" width="210"> \| 1373 \| 83.1 \| 49.1 \|
	\| `china-southwest` \| <img src="regional/china-southwest/coverage.png" width="210"> \| 1409 \| 84.0 \| 49.3 \|
	\| `tibet` \| <img src="regional/tibet/coverage.png" width="210"> \| 1407 \| 83.9 \| 49.3 \|

	### North America

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `north-america-east` \| <img src="regional/north-america-east/coverage.png" width="210"> \| 999 \| 73.9 \| 46.8 \|
	\| `north-america-west` \| <img src="regional/north-america-west/coverage.png" width="210"> \| 1002 \| 74.0 \| 46.8 \|
	\| `canada-alaska` \| <img src="regional/canada-alaska/coverage.png" width="210"> \| 962 \| 73.0 \| 46.5 \|

	### South America

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `amazonia` \| <img src="regional/amazonia/coverage.png" width="210"> \| 3388 \| 132.7 \| 61.5 \|
	\| `andes` \| <img src="regional/andes/coverage.png" width="210"> \| 3535 \| 136.3 \| 62.4 \|
	\| `eastern-brazil` \| <img src="regional/eastern-brazil/coverage.png" width="210"> \| 2184 \| 103.0 \| 54.1 \|
	\| `southern-cone` \| <img src="regional/southern-cone/coverage.png" width="210"> \| 1855 \| 95.0 \| 52.0 \|
	\| `galapagos` \| <img src="regional/galapagos/coverage.png" width="210"> \| 336 \| 57.6 \| 42.7 \|

	### Africa

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `southern-africa` \| <img src="regional/southern-africa/coverage.png" width="210"> \| 1002 \| 74.0 \| 46.8 \|
	\| `reunion` \| <img src="regional/reunion/coverage.png" width="210"> \| 274 \| 56.1 \| 42.3 \|
	\| `mauritius` \| <img src="regional/mauritius/coverage.png" width="210"> \| 272 \| 56.0 \| 42.3 \|
	\| `seychelles` \| <img src="regional/seychelles/coverage.png" width="210"> \| 318 \| 57.2 \| 42.6 \|
	\| `cape-verde` \| <img src="regional/cape-verde/coverage.png" width="210"> \| 346 \| 57.8 \| 42.7 \|
	\| `sao-tome-principe` \| <img src="regional/sao-tome-principe/coverage.png" width="210"> \| 324 \| 57.3 \| 42.6 \|
	\| `west-africa` \| <img src="regional/west-africa/coverage.png" width="210"> \| 1309 \| 81.5 \| 48.7 \|
	\| `central-africa` \| <img src="regional/central-africa/coverage.png" width="210"> \| 1883 \| 95.6 \| 52.2 \|
	\| `east-africa` \| <img src="regional/east-africa/coverage.png" width="210"> \| 2159 \| 102.4 \| 53.9 \|

	### Oceania

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `australia-east` \| <img src="regional/australia-east/coverage.png" width="210"> \| 946 \| 72.6 \| 46.4 \|
	\| `new-zealand` \| <img src="regional/new-zealand/coverage.png" width="210"> \| 486 \| 61.3 \| 43.6 \|
	\| `hawaii` \| <img src="regional/hawaii/coverage.png" width="210"> \| 478 \| 61.1 \| 43.6 \|
	\| `new-caledonia` \| <img src="regional/new-caledonia/coverage.png" width="210"> \| 377 \| 58.6 \| 42.9 \|

	## Usage

	Each model takes 5 s of 32 kHz mono audio (`[1, 160000]`) and outputs a `label` vector of logits over its species list; pair it with the sibling `*_labels.txt` (line count matches the logit count). Pick a variant by hardware (table above). Confidence is a softmax over the model's own classes, so a regional tile normalizes over fewer species than the full model; recalibrate detection thresholds per model.

	## Provenance

	Regional slices are gathered from the ProtoPNet head of `perch_v2_no_dft.onnx` and validated bit-exact against the full model on the species they keep. Perch v2 is by Google; see the license above.

	---
	pretty_name: Perch v2 Models (full + regional catalog)
	license: apache-2.0
	base_model:
	- cgeorgiaw/Perch
	tags:
	- bioacoustics
	- birdnet
	- perch
	- bird
	- vocalization
	- audio-classification
	- onnx
	library_name: onnx
	---

	# Perch v2 Models (full + regional catalog)

	Google's Perch v2 bioacoustic classifier in three deployment variants, plus a catalog of region-specific slices that are smaller and faster while staying numerically identical (bit-exact) to the full model on the species they keep.

	## Origin and attribution

	- Perch v2 by Google Research ([bird-vocalization-classifier](https://www.kaggle.com/models/google/bird-vocalization-classifier/)): EfficientNet-B3, ~12M embedding + ~91M classification params, ~15,000 species.
	- ONNX conversion and the DFT-to-MatMul (`no_dft`) optimization by [justinchuby](https://huggingface.co/justinchuby/Perch-onnx).
	- Labels from [cgeorgiaw/Perch](https://huggingface.co/cgeorgiaw/Perch) (iNaturalist taxonomy).
	- Regional slicing uses the BirdNET Geomodel v3.0 range filter (birdnet-team/geomodel) to pick each region's species.

	## Variants and hardware

	\| filename token \| precision \| best for \|
	\|---\|---\|---\|
	\| `_fp32` \| FP32, with DFT \| GPU (CUDA/TensorRT), Intel CPU \|
	\| `_no_dft_fp32` \| FP32, DFT removed \| OpenVINO (RPi5 fast path); also runs on ORT/CUDA \|
	\| `_int8_arm` \| partial INT8 (MatMul-only) \| ARM CPU / Raspberry Pi, low RAM \|

	## Full model

	\| file \| MB \|
	\|---\|---:\|
	\| `full/perch_v2_fp32.onnx` \| 409 \|
	\| `full/perch_v2_no_dft_fp32.onnx` \| 413 \|
	\| `full/perch_v2_int8_arm.onnx` \| 131 \|
	\| `full/perch_v2_labels.txt` \| 14,795 classes \|

	## Why regional models

	These slices are built for real-time detection on resource-constrained devices, phones, Raspberry Pi and other single-board computers, where running the full 14,795-class Perch v2 continuously is costly in both RAM and CPU. Most of that cost goes to recognising species that cannot occur at the listener's location. Restricting the model to a region's species shrinks the memory footprint and the per-inference compute (the classifier head is ~88% of the model), so an always-on detector keeps up with the live audio stream and leaves headroom for the rest of the application, on hardware where the full model would struggle. Each tile stays bit-exact to the full model on the species it keeps; the only change is that out-of-region species are not emitted, which at a fixed monitoring location is exactly what you want.

	## Regional catalog

	Each tile: BirdNET Geomodel v3.0 range filter (top ~800 species for temperate regions, ~1200 for bird-rich tropical/subtropical ones, up to ~3500 for the hyper-diverse Neotropics) + 198 FSD50K sound events + a 27-species cosmopolitan core. Ships `_no_dft_fp32` (OpenVINO/GPU) and `_int8_arm` (ARM) + labels + indices. All bit-exact vs the full model on the species they keep.

	Each tile folder also has `coverage.png` (a map of the region it covers) and `metadata.json` (species count, covered countries, and the continental `group` it belongs to).

	Tiles are organised by continent below. `regional/groups.json` lists the same grouping (ordered continents -> tiles) in one file, and each tile's `metadata.json` carries its `group` / `group_display` / `group_order`, so an application can rebuild these sections without scraping this table.

	### Europe

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `nordic` \| <img src="regional/nordic/coverage.png" width="210"> \| 638 \| 65.0 \| 44.5 \|
	\| `british-isles` \| <img src="regional/british-isles/coverage.png" width="210"> \| 776 \| 68.4 \| 45.4 \|
	\| `central-europe` \| <img src="regional/central-europe/coverage.png" width="210"> \| 873 \| 70.8 \| 46.0 \|
	\| `baltics` \| <img src="regional/baltics/coverage.png" width="210"> \| 655 \| 65.4 \| 44.6 \|
	\| `iberia` \| <img src="regional/iberia/coverage.png" width="210"> \| 856 \| 70.4 \| 45.9 \|
	\| `southern-europe` \| <img src="regional/southern-europe/coverage.png" width="210"> \| 839 \| 70.0 \| 45.8 \|
	\| `eastern-europe` \| <img src="regional/eastern-europe/coverage.png" width="210"> \| 739 \| 67.5 \| 45.2 \|
	\| `western-palearctic` \| <img src="regional/western-palearctic/coverage.png" width="210"> \| 1599 \| 88.7 \| 50.5 \|
	\| `iceland` \| <img src="regional/iceland/coverage.png" width="210"> \| 591 \| 63.9 \| 44.3 \|
	\| `svalbard` \| <img src="regional/svalbard/coverage.png" width="210"> \| 480 \| 61.1 \| 43.6 \|
	\| `canary-islands` \| <img src="regional/canary-islands/coverage.png" width="210"> \| 598 \| 64.0 \| 44.3 \|
	\| `madeira` \| <img src="regional/madeira/coverage.png" width="210"> \| 459 \| 60.6 \| 43.4 \|
	\| `azores` \| <img src="regional/azores/coverage.png" width="210"> \| 426 \| 59.8 \| 43.2 \|

	### Asia

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `south-asia-peninsular` \| <img src="regional/south-asia-peninsular/coverage.png" width="210"> \| 879 \| 70.9 \| 46.0 \|
	\| `indo-gangetic` \| <img src="regional/indo-gangetic/coverage.png" width="210"> \| 1406 \| 83.9 \| 49.3 \|
	\| `himalaya` \| <img src="regional/himalaya/coverage.png" width="210"> \| 1407 \| 83.9 \| 49.3 \|
	\| `japan` \| <img src="regional/japan/coverage.png" width="210"> \| 799 \| 69.0 \| 45.5 \|
	\| `china-northeast` \| <img src="regional/china-northeast/coverage.png" width="210"> \| 877 \| 70.9 \| 46.0 \|
	\| `china-north-central` \| <img src="regional/china-north-central/coverage.png" width="210"> \| 976 \| 73.3 \| 46.6 \|
	\| `china-southeast` \| <img src="regional/china-southeast/coverage.png" width="210"> \| 1373 \| 83.1 \| 49.1 \|
	\| `china-southwest` \| <img src="regional/china-southwest/coverage.png" width="210"> \| 1409 \| 84.0 \| 49.3 \|
	\| `tibet` \| <img src="regional/tibet/coverage.png" width="210"> \| 1407 \| 83.9 \| 49.3 \|

	### North America

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `north-america-east` \| <img src="regional/north-america-east/coverage.png" width="210"> \| 999 \| 73.9 \| 46.8 \|
	\| `north-america-west` \| <img src="regional/north-america-west/coverage.png" width="210"> \| 1002 \| 74.0 \| 46.8 \|
	\| `canada-alaska` \| <img src="regional/canada-alaska/coverage.png" width="210"> \| 962 \| 73.0 \| 46.5 \|

	### South America

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `amazonia` \| <img src="regional/amazonia/coverage.png" width="210"> \| 3388 \| 132.7 \| 61.5 \|
	\| `andes` \| <img src="regional/andes/coverage.png" width="210"> \| 3535 \| 136.3 \| 62.4 \|
	\| `eastern-brazil` \| <img src="regional/eastern-brazil/coverage.png" width="210"> \| 2184 \| 103.0 \| 54.1 \|
	\| `southern-cone` \| <img src="regional/southern-cone/coverage.png" width="210"> \| 1855 \| 95.0 \| 52.0 \|
	\| `galapagos` \| <img src="regional/galapagos/coverage.png" width="210"> \| 336 \| 57.6 \| 42.7 \|

	### Africa

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `southern-africa` \| <img src="regional/southern-africa/coverage.png" width="210"> \| 1002 \| 74.0 \| 46.8 \|
	\| `reunion` \| <img src="regional/reunion/coverage.png" width="210"> \| 274 \| 56.1 \| 42.3 \|
	\| `mauritius` \| <img src="regional/mauritius/coverage.png" width="210"> \| 272 \| 56.0 \| 42.3 \|
	\| `seychelles` \| <img src="regional/seychelles/coverage.png" width="210"> \| 318 \| 57.2 \| 42.6 \|
	\| `cape-verde` \| <img src="regional/cape-verde/coverage.png" width="210"> \| 346 \| 57.8 \| 42.7 \|
	\| `sao-tome-principe` \| <img src="regional/sao-tome-principe/coverage.png" width="210"> \| 324 \| 57.3 \| 42.6 \|
	\| `west-africa` \| <img src="regional/west-africa/coverage.png" width="210"> \| 1309 \| 81.5 \| 48.7 \|
	\| `central-africa` \| <img src="regional/central-africa/coverage.png" width="210"> \| 1883 \| 95.6 \| 52.2 \|
	\| `east-africa` \| <img src="regional/east-africa/coverage.png" width="210"> \| 2159 \| 102.4 \| 53.9 \|

	### Oceania

	\| region \| coverage \| classes \| fp32 MB \| int8-arm MB \|
	\|---\|---\|---:\|---:\|---:\|
	\| `australia-east` \| <img src="regional/australia-east/coverage.png" width="210"> \| 946 \| 72.6 \| 46.4 \|
	\| `new-zealand` \| <img src="regional/new-zealand/coverage.png" width="210"> \| 486 \| 61.3 \| 43.6 \|
	\| `hawaii` \| <img src="regional/hawaii/coverage.png" width="210"> \| 478 \| 61.1 \| 43.6 \|
	\| `new-caledonia` \| <img src="regional/new-caledonia/coverage.png" width="210"> \| 377 \| 58.6 \| 42.9 \|

	## Usage

	Each model takes 5 s of 32 kHz mono audio (`[1, 160000]`) and outputs a `label` vector of logits over its species list; pair it with the sibling `*_labels.txt` (line count matches the logit count). Pick a variant by hardware (table above). Confidence is a softmax over the model's own classes, so a regional tile normalizes over fewer species than the full model; recalibrate detection thresholds per model.

	## Provenance

	Regional slices are gathered from the ProtoPNet head of `perch_v2_no_dft.onnx` and validated bit-exact against the full model on the species they keep. Perch v2 is by Google; see the license above.