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README.md

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+---
+license: other
+license_name: countgdplusplus-research
+license_link: https://github.com/niki-amini-naieni/CountGDPlusPlus
+tags:
+  - object-counting
+  - litter
+  - open-vocabulary
+  - sam3
+base_model: niki-amini-naieni/CountGDPlusPlus
+---
+
+# CountGD++ Litter — dense fine-tune (`countgd_litter_dense.pth`)
+
+CountGD++ fine-tuned to count litter in photos, including extremely dense illegal-dumping piles.
+
+- **Prompt:** `litter` · recommended confidence 0.23
+- **Backbone/BERT frozen;** detection head fine-tuned with token focal loss + L1/GIoU on SAM 3 pseudo-labels
+- **Teacher:** Meta SAM 3 (`facebook/sam3`), four-prompt union (`litter, trash, garbage, discarded packaging`), with **4×4 tiled inference** on human-flagged extreme-density images (tiled teacher mean 84 items vs 25 untiled on those scenes)
+- **Training set:** 954 human-triaged images (194 extreme-density, 246 invalid images removed by review)
+- **Holdout (200, stratified):** overall count MAE 10.9; dense MAE 51 → 17; normal MAE 9.5
+- **48-image comparison:** mean count 65.8 (previous dense-blind fine-tune: 33.6; untuned CountGD++: 1.9)
+
+## Checkpoint format
+
+`torch.load(path, map_location="cpu", weights_only=False)` →
+`{"model": state_dict, "epoch": 2, "prompt": "litter", "holdout": [...]}`.
+Load `ckpt["model"]` into CountGD++ (`countgd_infer.py` in https://github.com/HugoMarchant/llitter-index).
+
+## Limits
+
+Counts are pseudo-label quality: SAM 3 still undercounts the very densest piles, and
+near-dense images see a small accuracy regression. Counts are capped by CountGD++'s
+query budget (~900). Trained on litter scenes; not a general object counter.
+
+See RESULTS.md in the code repository for the full report.

countgd_litter_dense.pth

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