Metadata-Version: 2.4
Name: attr-eomt
Version: 1.1.0
Summary: Standalone EoMT (Encoder-only Mask Transformer) for instance segmentation, with DINOv2 init, COCO training/validation and inference.
Project-URL: Homepage, https://github.com/imagra93/attr-eomt
Project-URL: Documentation, https://imagra93.github.io/attr-eomt
Project-URL: Repository, https://github.com/imagra93/attr-eomt
Project-URL: Issues, https://github.com/imagra93/attr-eomt/issues
Author: attr-eomt contributors
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: coco,dinov2,eomt,instance-segmentation,transformers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Dist: huggingface-hub>=0.23.0
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Requires-Dist: opencv-python>=4.8.0
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Requires-Dist: scipy>=1.7.0
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Requires-Dist: torchao>=0.7.0
Requires-Dist: torchvision>=0.19.0
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Requires-Dist: transformers>=5.1.0
Provides-Extra: dev
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Requires-Dist: pytest>=7.0; extra == 'dev'
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Provides-Extra: logging
Requires-Dist: tensorboard>=2.10; extra == 'logging'
Requires-Dist: wandb>=0.15; extra == 'logging'
Description-Content-Type: text/markdown

<div align="center">

<img src="https://raw.githubusercontent.com/imagra93/attr-eomt/main/docs/assets/00-hero-banner.png" alt="attr-eomt — one DINOv2 encoder predicts instances plus independent per-instance attribute heads in a single pass, contrasted with flat combinatorial labels and a detector-plus-second-model pipeline" width="100%">

<p>
  <a href="https://pypi.org/project/attr-eomt/"><img src="https://img.shields.io/pypi/v/attr-eomt.svg?color=4ec9b0" alt="PyPI version"></a>
  <a href="https://pypi.org/project/attr-eomt/"><img src="https://img.shields.io/pypi/pyversions/attr-eomt.svg" alt="Python versions"></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue.svg" alt="License"></a>
  <a href="https://imagra93.github.io/attr-eomt"><img src="https://img.shields.io/badge/docs-annotated%20explainer-e2b341.svg" alt="Annotated explainer"></a>
</p>

**One query embedding, many independent labels.**

📖 **[Read the annotated explainer →](https://imagra93.github.io/attr-eomt)**

</div>

---

## What it is

**attr-eomt** is a standalone **EoMT** (Encoder-only Mask Transformer) for **instance
segmentation** and **object detection**, with one feature that sets it apart: **independent
per-instance attribute heads**. Alongside the mask/box + class output, it predicts one or
several orthogonal attributes for *every* detected instance — read straight off the **same**
per-query embedding the detector already computes. No second model, no second pass, and the
primary detection metric is untouched (the figure above tells the whole story).

It's a clean-room, Apache-2.0 reimplementation: the weights you train are yours to release.

```python
from eomt import EoMT

model = EoMT("l")                            # fresh large model (DINOv2 backbone)
model.train(data="coco", epochs=50)          # COCO 2017 auto-downloads if missing

model = EoMT("runs/train/eomt-l")            # reload a run — size/classes/heads auto-detected
model.predict("images/", plot=True)          # render masks/boxes + per-instance attributes
```

---

## Architecture

EoMT is a **DINOv2-with-registers ViT** whose last few transformer blocks are augmented
with a fixed set of **learnable queries** (the Mask2Former idea) — each query is one
"slot" that latches onto one object instance. After the encoder runs, every query emits
a single vector, the **per-query embedding** of shape `[B, Q, hidden]`. The whole model
is then just "turn that embedding into predictions": a **class head** for the primary
label and a **mask/box head** for geometry. It is **NMS-free**, so two overlapping
garments stay two distinct queries instead of being merged — the property that lets
attributes stay attached to the right instance.

The attribute heads add nothing to this picture except themselves: they tap the **exact
same embedding** (captured non-invasively with a forward hook), each a small classifier
on top.

This collapses what is classically a *two-stage* pipeline — detect, crop each box, run a
second classifier per crop — into a single pass. Attributes therefore cost only a thin head
each, see **full-image context** (not just a cropped box), and never inherit a second
model's cropping errors — the modern, single-stage formulation of the DETR / Mask2Former
lineage (see the figure at the top).

### Two model families: segmentation & detection

Both families share the same DINOv2 encoder, query mechanism, NMS-free matching and
auxiliary heads — they differ only in the head on top and what they output:

| family | `--task` | output | metric driving `best.pt` |
|--------|----------|--------|--------------------------|
| **instance** (default) | `instance` | per-instance **masks** + boxes + class | `segm/mAP` |
| **detect** | `detect`  | per-instance **boxes** + class (DETR-style box head, no masks) | `bbox/mAP` |

```python
EoMT("l").train(data="coco", family="instance")   # masks (default)
EoMT("l").train(data="coco", family="detect")     # boxes only
```

The family is recorded in the checkpoint, so `val` / `predict` pick the right
post-processing automatically. Everything below applies identically to both.

### Models & sizes

| size | backbone        | hidden | layers | heads | queries |
|------|-----------------|--------|--------|-------|---------|
| `s`  | DINOv2-small    | 384    | 12     | 6     | 100     |
| `b`  | DINOv2-base     | 768    | 12     | 12    | 200     |
| `l`  | DINOv2-large    | 1024   | 24     | 16    | 200     |

Default input is a patch-14-aligned square (`644 = 14 × 46`) so DINOv2 weights load 1:1.

### Compute & inference speed

Measured on a single **NVIDIA GeForce RTX 5090**, `644 × 644` input, batch size 1.
GFLOPs are multiply-accumulates at that resolution (attention included); latency /
throughput are the median over 50 runs after warm-up, under `torch.amp.autocast`
(fp16) — the package's own inference path.

**`instance` family** (masks + boxes + class):

| size | params | GFLOPs | latency (fp16) | throughput (fp16) | throughput (fp32) |
|------|--------|--------|----------------|-------------------|-------------------|
| `s`  | 24.0 M | 128    | 8.4 ms         | 119 img/s         | 70 img/s          |
| `b`  | 93.9 M | 430    | 17.4 ms        | 58 img/s          | 32 img/s          |
| `l`  | 317 M  | 1144   | 30.2 ms        | 33 img/s          | 15 img/s          |

**`detect` family** (boxes + class, no mask head):

| size | params | GFLOPs | latency (fp16) | throughput (fp16) | throughput (fp32) |
|------|--------|--------|----------------|-------------------|-------------------|
| `s`  | 22.7 M | 89     | 2.9 ms         | 348 img/s         | 120 img/s         |
| `b`  | 88.6 M | 276    | 5.3 ms         | 190 img/s         | 60 img/s          |
| `l`  | 308 M  | 881    | 13.6 ms        | 74 img/s          | 21 img/s          |

Dropping the mask-upsampling head makes `detect` substantially lighter and ~1.3–3×
faster. Figures are for the detector itself (backbone + queries + heads); the
attribute heads add a thin linear/MLP per head and are negligible by design.

---

## Factorizing the label space

This is the contribution. Conventional detectors fold every distinction into one flat
label space: an object's `type × viewpoint × occlusion × …` becomes a Cartesian product of
leaf classes that explodes combinatorially, starves each leaf of examples, and multiplies
the Hungarian matcher's targets. **attr-eomt factorizes instead** — a small, general primary
head plus independent attribute heads that **add, not multiply**.

Because the heads are independent, the primary taxonomy stays compact and every class keeps
its full sample count; attributes ride along for near-zero compute; and the model composes
`attribute × class` combinations that **never appear in the training data** — combinations a
flat label space cannot even represent.

### Example: clothing with per-instance attributes

One model segments each garment (primary classes like `vest_dress` / `short_sleeve_top`
/ `long_sleeve_dress` / `skirt` / `trousers` …) and, for **every** detection, reads off
four **independent** attribute heads — `scale` (`small` / `modest` / `large`),
`occlusion` (`no` / `slight` / `medium`), `zoom_in` (`no` / `medium` / `large`) and
`viewpoint` (`frontal` / `side` / `back`). The renderer prints the primary class + score
on the first row and each attribute + its confidence on the rows beneath it.

![Two people in dresses; each instance labelled with its garment class plus scale, occlusion, zoom and viewpoint attributes](https://raw.githubusercontent.com/imagra93/attr-eomt/main/docs/examples/sample.jpg)

The four attributes are *orthogonal* to the garment class — they vary independently —
which is exactly the case that's awkward to fold into the primary class space. The same
pattern fits any "class **plus** per-instance sub-labels" task: **retail shelves →
product + facing**, **documents → element + role**, **cells → type + health**.

> Trained on the public **[DeepFashion2](https://github.com/switchablenorms/DeepFashion2)**
> dataset (13 garment classes + 4 attribute heads) and rendered with the package's own
> renderer ([`eomt.visualize.draw_instances`](eomt/visualize.py)).

---

## Training — it rides on the detector's own match

Attributes never run their own matcher. Detection already solves "which query is
responsible for which ground-truth object" via the **Hungarian matcher**; attributes
simply reuse that same query→GT assignment and read the answer off the matched queries.

- **Embedding source.** Each head reads the per-query embedding — the input to EoMT's
  `class_predictor`, captured with a forward hook (`[B, Q, hidden]`).
- **Matching.** Supervision reuses EoMT's *own* Hungarian matcher
  (`model.eomt.criterion.matcher`), so every attribute is trained on the **same**
  query→GT assignment the detection loss used; the attribute is read *after* matching.
- **Gate.** An optional IoU gate drops barely-overlapping matched pairs (common early in
  training) so attributes only learn from queries that actually localize the object.
- **Loss.** Cross-entropy per head over matched queries, summed across heads and scaled
  by `aux_w` (default `1.0`), added to the detector loss. Empty-match batches contribute
  a graph-preserving zero, and missing labels use `ignore_index` and contribute nothing.
- **Checkpoint selection is unchanged.** The attribute "rides along": its per-head
  matched-query accuracy is shown live and written to `metrics.csv`, but never drives
  `best.pt` (still `segm/mAP` or `bbox/mAP`).
- **Inference.** Each result attaches `aux = {head: {"ids", "probs"}}` for the kept
  detections, and `predict(plot=True)` renders each attribute next to the class label
  using names stored in the checkpoint.

---

## Data format (auto-discovered from the COCO JSON)

Attributes live **inside the COCO annotations** — each annotation is already a
per-instance object, so alignment is automatic and `pycocotools` still parses it. Just
two additions to a standard COCO file; **no YAML changes** — heads (count, classes,
names) are discovered from the JSON, the same as `nc`.

**1. A top-level `attributes` list** — one entry per head, defining its vocabulary:

```jsonc
"attributes": [
  {
    "name": "scale",
    "categories": [
      {"id": 1, "name": "small"},
      {"id": 2, "name": "modest"},
      {"id": 3, "name": "large"}
    ]
  },
  {
    "name": "viewpoint",
    "categories": [
      {"id": 0, "name": "frontal"},
      {"id": 1, "name": "side"},
      {"id": 2, "name": "back"}
    ]
  }
]
```

**2. A per-annotation `attributes` map** — `{head: raw_id}` on each instance:

```jsonc
{
  "id": 1, "image_id": 42, "category_id": 1,
  "segmentation": [...], "bbox": [...], "area": 1234, "iscrowd": 0,
  "attributes": {"scale": 3, "viewpoint": 0}
}
```

Notes:

- Raw ids are remapped to a contiguous `0..n-1` per head (so `scale`'s `1`/`2`/`3` become
  `0`/`1`/`2`); `categories` may be omitted, in which case the id set is inferred.
- A **missing or out-of-vocab** per-annotation value is *ignored* (`-100`), not trained as
  class `0` — so a **partially tagged** dataset is valid: each head learns only from the
  instances that actually carry its value. A JSON with **no** `attributes` ⇒ detection-only,
  exactly as before.

**Class-conditional heads.** Give an attribute definition an optional `applies_to` list of
primary-class names or ids, and that head is only trained on — and only emitted for —
instances of those classes (hard routing on the primary class). Omit it and the head applies
to every class. So different attributes can attach to different classes, each with its own
label set, in one model:

```jsonc
"attributes": [
  {"name": "posture", "categories": [...], "applies_to": ["cat", "dog"]}
]
```

At inference a scoped head reports `ids = -1` ("not applicable") for detections whose class it
does not cover. The scope is stored in the checkpoint, so it survives reload.

**Sidecar format (optional).** You can keep the COCO JSON as plain, standard COCO and put the
attributes beside it instead of inside it: an `attributes.yaml` schema in the dataset root plus
`attributes/<split>.json` values keyed by annotation id (`{ann_id: {head: value}}`). If present
(and the JSON has no embedded `attributes`), it is merged in memory at load — so a plain COCO
dataset always works and the sidecar is picked up automatically when you add it. Embedded
`attributes` in the JSON take precedence.

A tiny, self-contained example (two heads, including a non-contiguous id set) lives in
[sample_data/](sample_data/).

---

## Install

```bash
pip install attr-eomt                  # from PyPI
pip install "attr-eomt[logging]"       # + tensorboard/wandb
pip install -e ".[dev]"                # from source (editable; [dev] adds pytest/build/twine)
```

## Usage

Everything goes through one class. Initialize from a **size** (fresh model, pretrained
DINOv2 backbone) or from a **checkpoint / run folder** (family, size, classes, image
size, normalization and any auxiliary heads are auto-detected from the `.pt`):

```python
from eomt import EoMT

# Train on COCO 2017 (auto-downloaded on first run):
EoMT("l").train(data="coco", epochs=50, batch=4)

# ...or any COCO-format dataset (point at its data.yaml):
EoMT("s").train(data="sample_data/data.yaml", epochs=1, batch=1)

# Validate and predict from a trained run:
EoMT("runs/train/eomt-l").val(data="coco")
EoMT("runs/train/eomt-l").predict("images/", plot=True)   # writes annotated images
```

For the full training recipe, every `train()` knob, and int8 compression, see the
**[annotated explainer →](https://imagra93.github.io/attr-eomt)** — it's the deep dive.

---

## Roadmap / future work

- **Model export.** ONNX / TensorRT (and friends) for deployment — currently out of
  scope; the inference path is being kept export-friendly.
- **Keypoints.** A keypoint/pose head family alongside `instance` and `detect` (the code
  already carries a `family` parameter so new heads slot in without API churn).
- **Pretrained COCO checkpoints.** None are published yet. COCO-trained `s`/`b`/`l`
  weights will be released on the Hugging Face Hub (the `from_pretrained` / `hf://`
  loading plumbing is already in place and waiting for them).
- **Multi-image re-ID via contrastive learning.** Train the auxiliary head with a
  contrastive objective so each instance's query embedding becomes a **re-identification
  vector** — matching the same object across images, frames and cameras for tracking and
  retrieval. The aux head already produces a per-instance embedding from the detector's
  own matched queries; re-ID reuses that signal instead of bolting on a separate model.
