Replace HF repo with V1-V6 models, app assets and updated card

README.md

@@ -1,6 +1,8 @@
 ---
 license: mit
 tags:
+  - image-feature-extraction
+  - LiteRT
   - wildlife
   - animal-re-identification
   - face-recognition
@@ -13,64 +15,82 @@ pipeline_tag: image-feature-extraction
 
 # OrangIdentifier
 
-**Individual facial recognition for Bornean orangutans** — end-to-end pipeline from raw photographs to offline Android deployment.
+Individual facial recognition for Bornean orangutans, from raw photographs to an offline Android deployment.
 
-Source code (ML Pipeline): [github.com/tit-exe/OrangIdentifier](https://github.com/tit-exe/OrangIdentifier)  
-Source code (Android App): [github.com/tit-exe/OrangIdentifier-Android](https://github.com/tit-exe/OrangIdentifier-Android)
+- Source code (ML pipeline): https://github.com/tit-exe/OrangIdentifier
+- Source code (Android app): https://github.com/tit-exe/OrangIdentifier_AndroidApp
 
 ## Overview
 
-This pipeline trains a face detector and an individual identification model from labeled photographs, then exports the result as a lightweight gallery JSON for an Android app that runs entirely offline.
-
-The gallery is a JSON file containing one averaged embedding vector per individual. Adding a new individual requires 10–20 photos, takes under a minute, and requires no retraining.
-
-## Android App Assets
-
-This repository also hosts the exported `.tflite` models and the gallery database required to run the **offline Android app**. 
-
-If you are building the Android app, download these three files from the `main` branch and place them in the `app/src/main/assets/` folder of the Android project:
-- `gallery.json`: The database containing the identity prototypes.
-- `yolo_v2_detector.tflite`: The optimized YOLO face detector model.
-- `megadesc_T_arcface_backbone.tflite`: The MegaDescriptor-T embedding backbone (112 MB).
+This project trains a face detector and an individual identification model from labeled
+photographs, then exports the result as a lightweight gallery JSON for an Android app that
+runs entirely offline. The gallery holds one embedding vector per individual. Adding a new
+individual requires 10 to 20 photos, takes under a minute, and requires no retraining.
 
 ## Inference pipeline
 
 ```
-Raw photo → YOLO face detection (mAP@50=99.4%)
-         → 224×224 crop
-         → MegaDescriptor-T-224 (Swin Transformer, 768-dim embedding)
-         → Cosine similarity vs gallery
-         → Known individual (sim ≥ 0.22) or Unknown (sim < 0.22)
+Raw photo -> YOLO v2 face detection (mAP@50 = 99.4%)
+          -> 224x224 crop
+          -> MegaDescriptor-T-224 (Swin Transformer, 768-dim embedding)
+          -> cosine similarity vs gallery
+          -> Known individual (sim >= threshold) or Unknown (sim < threshold)
 ```
 
+## Android app assets
+
+This repository also hosts the exported models and the gallery database required to run the
+offline Android app. To build the app, download these three files from the `main` branch and
+place them in `app/src/main/assets/` of the Android project:
+
+- `gallery.json` : the identity database (embedding prototypes).
+- `yolo_v2_detector.tflite` : the YOLO face detector.
+- `megadesc_v6_backbone.tflite` : the V6 MegaDescriptor-T embedding backbone (112 MB).
+
 ## Models
 
-| File | Used in | Size | Description |
+| File | Version | Size | Description |
 |------|---------|------|-------------|
-| `yolo_v1_nano_mAP92.pt` | V1 | 6 MB | YOLO nano — mAP@50 = 91.98% |
-| `yolo_v2_medium_mAP99.pt` | V1–V4 | 85 MB | YOLO medium — mAP@50 = 99.39% |
-| `resnet50_classifier_10classes_acc96.pt` | V1 | 90 MB | Closed-set classifier, acc = 96.3% |
+| `yolo_v1_nano_mAP92.pt` | V1 | 6 MB | YOLO nano, mAP@50 = 91.98% |
+| `yolo_v2_medium_mAP99.pt` | V1 to V6 | 85 MB | YOLO medium, mAP@50 = 99.39% |
+| `resnet50_classifier_10classes_acc96.pt` | V1 | 90 MB | Closed-set classifier |
 | `resnet50_backbone_2048dim.pt` | V2 | 90 MB | Embedding backbone, 2048-dim |
 | `megadesc_T_arcface_final_epoch21_acc99.pt` | V3 | 105 MB | ArcFace, 10 individuals |
-| `megadesc_T_arcface_v4_40individuals_acc99.pt` | V4 ★ | 105 MB | ArcFace, 40 individuals |
+| `megadesc_T_arcface_v4_40individuals_acc99.pt` | V4 | 105 MB | ArcFace, 40 individuals |
+| `megadesc_T_arcface_v5_invariance_acc99.pt` | V5 | 105 MB | ArcFace + invariance, 40 individuals |
+| `megadesc_T_arcface_v6_15ind_acc98.pt` | **V6 (production)** | 105 MB | Zoo only, 15 individuals, deployed |
+| `megadesc_v6_backbone.tflite` | V6 (app) | 112 MB | V6 backbone for the Android app |
+| `yolo_v2_detector.tflite` | app | 22 MB | YOLO detector for the Android app |
+| `gallery.json` | app | 6.3 MB | Identity database for the Android app |
 
 ## Performance
 
-|  | V1 | V2 | V3 | **V4** |
-|--|----|----|----|----|
-| Backbone | ResNet50 | ResNet50 | MegaDescriptor-T | **MegaDescriptor-T** |
-| Supervised individuals | 10 | 10 | 10 | **40** |
-| Closed-set accuracy | 96.3% | ~98% | 99.2% | **99.2%** |
-| Unknown rejection (1,622 unseen crops) | — | 27.5% | 97.5% | **97.5%** |
-| Wild internet rejection | — | 48.5% | 93.2% | **93.0%** |
-| Separability gap | — | 0.294 | 0.883 | **0.885** |
+The versions were compared with a single fair evaluation: same session-level train/test split,
+galleries rebuilt identically, and each version scored for open-set rejection only on identities
+it never saw during training. Numbers below are on the 10 zoo individuals common to every version.
+
+| | V1 | V2 | V3 | V4 | V5 | V6 |
+|---|---|---|---|---|---|---|
+| Backbone | ResNet50 | ResNet50 | MegaDescriptor-T | MegaDescriptor-T | MegaDescriptor-T | MegaDescriptor-T |
+| Supervised individuals | 10 | 10 | 10 | 40 | 40 | 15 |
+| Clean identification | 96.5% | 96.5% | 99.2% | 99.2% | 99.7% | 99.2% |
+| Separability gap | 0.23 | 0.23 | 0.85 | 0.86 | 0.91 | 0.88 |
+| Unknown rejection (ROC AUC) | 0.83 | 0.83 | 0.998 | 0.99 | 0.99 | 0.999 |
+| Identification under moderate blur | 77% | 77% | 11% | 11% | 95% | 93% |
+
+Notes. A version is only scored for rejection on individuals it never learned: V1, V2, V3 and V6
+against the rescue-center (BOS) animals, V4 and V5 against the 5 new zoo individuals (they were
+trained on the BOS animals, so a BOS rejection figure would be data leakage). Clean identification
+on good zoo crops saturates from V3 onward; the real separation between versions appears under
+degradation, where the invariance training introduced in V5 keeps V5 and V6 above 90% while V3 and
+V4 fall to chance. V6 is the deployed production model.
 
 ## Dataset
 
 | Source | Individuals | Crops | Role |
 |--------|-------------|-------|------|
-| Captive collection | 10 | 2,127 | Training (known) |
-| Field rescue center | 30 | 1,622 | Open-set test only |
+| Captive collection (zoo) | 15 | 2,127 + 865 | Training (known) |
+| Field rescue center (BOS) | 30 | 1,622 | Supervised in V4/V5, unknown test set for V3/V6 |
 | Internet (iNaturalist, GBIF, web) | unlabeled | 5,429 | Background class |
 
 Images are not included.
@@ -82,20 +102,21 @@ from huggingface_hub import hf_hub_download
 
 path = hf_hub_download(
     repo_id="tit0000/OrangIdentifier",
-    filename="megadesc_T_arcface_v4_40individuals_acc99.pt"
+    filename="megadesc_T_arcface_v6_15ind_acc98.pt",
 )
 ```
 
 Or via the pipeline:
+
 ```bash
-python models/download_models.py
+python models/download_models.py --version all
 ```
 
 ## Security note
 
-These `.pt` files are standard PyTorch/Ultralytics checkpoints. The pickle imports
-flagged by HuggingFace are all from trusted libraries (torch, ultralytics, collections)
-and contain no malicious code.
+These `.pt` files are standard PyTorch and Ultralytics checkpoints. The pickle imports flagged by
+Hugging Face come from trusted libraries (torch, ultralytics, collections) and contain no malicious
+code.
 
 ## References
 
@@ -103,4 +124,4 @@ and contain no malicious code.
 - Deng et al. (2019). ArcFace. CVPR 2019.
 - Deng et al. (2020). Sub-center ArcFace. ECCV 2020.
 - Liu et al. (2021). Swin Transformer. ICCV 2021.
-- Jocher et al. (2023). Ultralytics YOLOv8.
+- Jocher et al. (2023). Ultralytics YOLO.

megadesc_T_arcface_v5_invariance_acc99.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4380d5ebb8df0028fd3319bdb81412e02a9e9322803df21293817f7969ca98a2
+size 110316482

megadesc_T_arcface_v6_15ind_acc98.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ebb1e04993afa73238db1885018072fd7de15a094da06b0bf8a89d70bbd23ea
+size 110252694

megadesc_T_arcface_backbone.tflite → megadesc_v6_backbone.tflite

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:045c91a822e22f60aecabfdcc749ecc35678e84824ad927d40f89271219936ef
+oid sha256:14775fd4593b8e621b9af8def66b3b1b8c4b32b6a5d446d05427e15770343e3d
 size 112476636