Add SAM3.1 NVFP4 detector (no-language, static-6) with model card
Browse files- .gitattributes +1 -0
- README.md +92 -0
- config.json +896 -0
- merges.txt +0 -0
- nvfp4_model.safetensors +3 -0
- processor_config.json +80 -0
- quant_error_report.json +258 -0
- quantization_config.json +0 -0
- sam3.1_multiplex.pt +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +33 -0
- vocab.json +0 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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assets/sam3.1_diagram.png filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
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@@ -0,0 +1,92 @@
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/sam3.1
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base_model_relation: quantized
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model_type: sam
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tags:
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- segmentation
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- sam
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- sam3.1
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- nvfp4
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- quantization
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- food-segmentation
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- instance-segmentation
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- video-segmentation
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inference: false
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---
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# SAM 3.1 NVFP4 — Detector (No Language), Static-6
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NVFP4 quantized variant of [facebook/sam3.1](https://huggingface.co/facebook/sam3.1).
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Quantizes the detector backbone's vision trunk while keeping the language backbone in FP32.
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## Quantization Details
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| Property | Value |
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|---|---|
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| Method | NVFP4 (`custom_nvfp4_e2m1_e4m3_scales`) |
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| Block size | 16 |
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| Scale rule | `static_6` |
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| Quantized scope | `detector.*` (vision trunk only) |
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| Language backbone | **FP32** (kept raw for prompt accuracy) |
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| Total parameters | 874 M |
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| Quantized parameters | 472 M (54.0%) |
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| FP32-kept parameters | 402 M (46.0%) |
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## Fidelity
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Validated against source SAM 3.1 on mukbang / food video frames with Sapiens-2 human-part exclusion masking:
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| Threshold | Mean foreground IoU | Pixel agreement |
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|---|---|---|
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| conf = 0.35 | **0.988** | **0.99897** |
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Some NVFP4 masks are visually sharper than the source FP32 output due to quantization-induced de-noising.
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## Files
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| File | Description |
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|---|---|
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| `nvfp4_model.safetensors` | NVFP4-packed detector tensors |
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| `sam3.1_multiplex.pt` | FP32 non-quantized tensors (language backbone, heads) |
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| `config.json` | SAM 3.1 model config |
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| `quantization_config.json` | NVFP4 packing metadata |
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| `quant_error_report.json` | Per-tensor L2 error report |
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| `tokenizer*.json / vocab.json / merges.txt` | Language-backbone tokenizer |
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## Usage
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```python
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# Load the quantized model using the nomnomlabel loader
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from nomnomlabel.quant_loader import load_sam3_nvfp4
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model = load_sam3_nvfp4("Reza2kn/sam3.1-nvfp4-detector-no-language")
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# Segment food (with language prompt)
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from nomnomlabel.sam3_food_classifier import SAM3FoodClassifier
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classifier = SAM3FoodClassifier(model_id="Reza2kn/sam3.1-nvfp4-detector-no-language")
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segments = classifier.segment_and_classify_food(image, conf_threshold=0.35)
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for seg in segments:
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print(f"Food: {seg.food_type} (conf={seg.food_conf:.2f})")
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print(f" Mask area: {seg.area} pixels")
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print(f" BBox: {seg.bbox}")
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```
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## Training / Benchmark Context
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Built as part of the **NomNomLabel Sapiens2 Benchmark** for food instance segmentation
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in long-form mukbang / eating content. The benchmark uses:
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- SAM 3.1 NVFP4 (this model) for food segmentation
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- Sapiens-2 INT4-G128 for human-part segmentation and exclusion masking
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- YOLO11L FoodSeg103 as a reference food detector
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## Paper
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[SAM 2: Segment Anything in Images and Videos](https://arxiv.org/abs/2408.00714)
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---
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*Quantized by [Reza2kn](https://huggingface.co/Reza2kn) using torchao NVFP4.*
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config.json
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@@ -0,0 +1,896 @@
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quant_error_report.json
ADDED
|
@@ -0,0 +1,258 @@
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sam3.1_multiplex.pt
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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vocab.json
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