2bf916f2aa
The head is now part of a store rather than a script: `finetune_identity.py --stock stock-lopia` trains on that store's enrolled views (27 views over 7 products, 12 augmented copies each) and writes `<stock>/identity_head.pt`, which ProductGallery loads automatically. The first attempt exposed a trap worth recording. A discriminative head has no "none of the above": trained with an angular margin to separate seven products, it maps everything confidently onto those seven, including cartons it has never seen. raw features, threshold 0.55 home shelf 78% accepted foreign shelves 5% accepted trained head, threshold 0.40 home shelf 58% accepted foreign shelves 44% accepted Rejection - the gallery's whole point - disappears. The fix needs no retraining: the two mechanisms fail in opposite directions, so raw features decide *whether* to answer (they generalise) and the head decides *what* to answer (it discriminates). Measured: 47% accepted on the home shelf, 5% on foreign ones. Lower coverage than raw-only, which is the honest trade. The head also needs its own threshold (0.40 against the raw 0.55): a trained head's similarity scale is its own, and reusing the raw line rejected almost everything it said. 175 tests pass. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
322 lines
14 KiB
Python
322 lines
14 KiB
Python
"""
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Fine-tune the identity embedding on the labels we can actually produce.
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Measured: off-the-shelf DINOv2 tops out at ~25 % on a real milk shelf where the
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products differ only by small text and a colour band, while it reaches 72-76 % on the
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demo shelf, whose facings are near-identical crops of one photograph. That gap is the
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whole problem, and the literature says the same thing (RP2K and RPC fine-tune;
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GroceryVision reports a 17.5-point drop from Recall@5 to Recall@1).
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This trains a small projection head on frozen DINOv2 features and compares, on the
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same held-out facings, three ways of naming a product:
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raw cosine the gallery as shipped
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fine-tuned cosine the same matching, on projected features
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fine-tuned + margin the same, trained with an angular margin
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**What this can and cannot show.** The labels come from one shelf, and the held-out
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facings are other facings of the same photograph, so the number is an upper bound: it
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measures whether a trained head separates these products at all, not whether it
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survives a second visit. Augmentation (crop, scale, brightness, blur) stands in for
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the visit we do not have; a cross-visit number needs cross-visit data.
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python code/finetune_identity.py [--epochs 60] [--views 2]
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"""
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from __future__ import annotations
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import argparse
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import os
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import sys
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import cv2
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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# Labelled by eye from a contact sheet of the detections (docs/EXTERNAL-EVAL.md).
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MILK_SHELF_LABELS = {
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"meiji_oishii": [13, 14, 15, 16, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],
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"tokunou45": [9, 10, 11, 12],
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"megmilk": [20, 21, 22, 23, 24, 25, 26, 37, 38, 46, 47, 54, 55, 56, 57, 58, 59,
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64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 90, 94, 95, 96, 97, 98, 99],
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"hokkaido": [49, 50, 51, 60, 61, 62, 63, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
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100, 101, 102, 103, 104],
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"teishibou": [2, 3, 4],
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"meiji_love": [5, 6, 7, 8],
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"oishii_tounyu": [42, 43, 44, 45],
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"rakunou36": [17, 18],
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}
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def build_model(image_size: int = 518):
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import timm
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import torch
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device = "mps" if torch.backends.mps.is_available() else "cpu"
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model = timm.create_model("vit_small_patch14_dinov2.lvd142m", pretrained=True,
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num_classes=0, img_size=image_size, dynamic_img_size=True)
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return model.eval().to(device), device
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def prepare(image, size: int = 518):
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mean = np.array([0.485, 0.456, 0.406], np.float32)
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std = np.array([0.229, 0.224, 0.225], np.float32)
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rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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height, width = rgb.shape[:2]
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side = max(height, width)
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canvas = np.zeros((side, side, 3), np.uint8)
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canvas[(side - height) // 2:(side - height) // 2 + height,
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(side - width) // 2:(side - width) // 2 + width] = rgb
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resized = cv2.resize(canvas, (size, size), interpolation=cv2.INTER_AREA).astype(np.float32) / 255.0
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return ((resized - mean) / std).transpose(2, 0, 1)
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def embed(model, device, images, size: int = 518, batch: int = 16):
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import torch
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out = []
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for start in range(0, len(images), batch):
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tensor = torch.from_numpy(np.stack([prepare(image, size) for image in images[start:start + batch]])).to(device)
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with torch.no_grad():
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features = model(tensor)
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out.append(torch.nn.functional.normalize(features, dim=1).cpu().numpy())
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return np.concatenate(out, axis=0)
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def augment(image, rng):
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"""Stand in for a second visit: crop, scale, brightness, contrast, blur."""
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height, width = image.shape[:2]
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# crop 82-100 % of the box, keeping the product centred
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keep = rng.uniform(0.82, 1.0)
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ch, cw = int(height * keep), int(width * keep)
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y = rng.randint(0, max(1, height - ch + 1))
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x = rng.randint(0, max(1, width - cw + 1))
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out = image[y:y + ch, x:x + cw]
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# photometric
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out = cv2.convertScaleAbs(out, alpha=float(rng.uniform(0.8, 1.2)), beta=float(rng.uniform(-18, 18)))
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if rng.random() < 0.4:
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out = cv2.GaussianBlur(out, (3, 3), 0)
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# small rotation
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if rng.random() < 0.6:
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angle = float(rng.uniform(-5, 5))
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matrix = cv2.getRotationMatrix2D((out.shape[1] / 2, out.shape[0] / 2), angle, 1.0)
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out = cv2.warpAffine(out, matrix, (out.shape[1], out.shape[0]), borderMode=cv2.BORDER_REPLICATE)
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return out
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def train_head(features, labels, class_ids, epochs, device, margin: float = 0.0, seed: int = 7):
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"""A cosine classifier on frozen features: small, and hard to overfit."""
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import torch
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torch.manual_seed(seed)
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dimension = features.shape[1]
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projection = torch.nn.Linear(dimension, 128).to(device)
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prototypes = torch.nn.Parameter(torch.randn(len(class_ids), 128, device=device))
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torch.nn.init.xavier_uniform_(prototypes)
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optimiser = torch.optim.AdamW(list(projection.parameters()) + [prototypes], lr=3e-3, weight_decay=1e-2)
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x = torch.from_numpy(features).float().to(device)
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y = torch.tensor([class_ids.index(label) for label in labels], device=device)
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for epoch in range(epochs):
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projected = torch.nn.functional.normalize(projection(x), dim=1)
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unit_prototypes = torch.nn.functional.normalize(prototypes, dim=1)
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logits = projected @ unit_prototypes.T
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if margin > 0:
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# angular margin: pull each sample away from the other prototypes
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one_hot = torch.nn.functional.one_hot(y, len(class_ids)).float()
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logits = logits - margin * one_hot
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loss = torch.nn.functional.cross_entropy(logits * 16.0, y, label_smoothing=0.05)
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optimiser.zero_grad()
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loss.backward()
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optimiser.step()
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return projection, torch.nn.functional.normalize(prototypes.detach(), dim=1)
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def train_from_stock(args) -> None:
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"""Train the head on a store's own enrolled views, for the gallery to load.
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The head belongs to the store: the views in its stock directory are its products,
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and a head trained on them is what turns the gallery from a lookup that cannot
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tell two milk cartons apart into one that can. One to four views per SKU is
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enough, with augmentation standing in for the rest.
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"""
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import glob
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import json
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import torch
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from gallery import ProductGallery
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stock = args.stock
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gallery = ProductGallery(stock, [])
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# the SKUs are whatever the stock directory holds
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skus = sorted({os.path.splitext(name)[0].rsplit("_", 1)[0] if os.path.splitext(name)[0].rsplit("_", 1)[-1].isdigit()
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else os.path.splitext(name)[0]
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for name in os.listdir(stock)
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if os.path.splitext(name)[1].lower() in (".jpeg", ".jpg", ".png")})
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views = {sku: gallery._views_for(sku) for sku in skus}
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views = {sku: paths for sku, paths in views.items() if paths}
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if len(views) < 2:
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print(f"{stock}: needs at least two products with views, found {len(views)}")
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return
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crops, labels = [], []
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for sku, paths in sorted(views.items()):
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for path in paths:
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image = cv2.imread(path)
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if image is not None and image.size:
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crops.append(image)
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labels.append(sku)
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class_ids = sorted(views)
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print(f"{stock}: {len(crops)} views over {len(class_ids)} products")
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model, device = build_model()
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rng = np.random.RandomState(11)
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augmented, augmented_labels = [], []
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for crop, label in zip(crops, labels):
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augmented.append(crop)
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augmented_labels.append(label)
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for _ in range(args.augment):
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augmented.append(augment(crop, rng))
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augmented_labels.append(label)
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print(f"embedding {len(augmented)} crops on {device}...")
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features = embed(model, device, augmented)
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projection, prototypes = train_head(features, augmented_labels, class_ids,
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args.epochs, device, margin=0.25)
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payload = {
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"class_ids": class_ids,
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"image_size": 518,
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"base_model": "vit_small_patch14_dinov2.lvd142m",
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"projection": {key: value.cpu() for key, value in projection.state_dict().items()},
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"prototypes": prototypes.cpu(),
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"views": {sku: len(paths) for sku, paths in sorted(views.items())},
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}
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path = os.path.join(stock, "identity_head.pt")
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torch.save(payload, path)
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print(f"wrote {path} ({len(class_ids)} products, {len(crops)} views)")
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--epochs", type=int, default=60)
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parser.add_argument("--views", type=int, default=2, help="enrolment views per class")
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parser.add_argument("--augment", type=int, default=12, help="augmented copies per enrolment view")
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parser.add_argument("--image", default=os.path.expanduser("~/dorayaki-data/shelf-images"))
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parser.add_argument("--stock", default=None,
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help="train on a store's enrolled views instead of the milk shelf, "
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"and write <stock>/identity_head.pt for the gallery to load")
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args = parser.parse_args()
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if args.stock:
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train_from_stock(args)
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return
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import json
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from engine import build_default_engine, EngineSettings
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manifest = json.load(open(os.path.join(args.image, "manifest.json")))
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entry = next(e for e in manifest["images"] if "2011年4月の牛乳" in e["file"])
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image = cv2.imread(os.path.join(args.image, entry["file"]))
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height, width = image.shape[:2]
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scale = 1600.0 / max(height, width)
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image = cv2.resize(image, (int(width * scale), int(height * scale)), interpolation=cv2.INTER_AREA)
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engine = build_default_engine(EngineSettings(stock_dir="stock"))
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boxes = engine.detect(image, conf=0.1)
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boxes.sort(key=lambda b: (b["box"][1] // 120, b["box"][0]))
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crops = [engine.crop(image, box["box"]) for box in boxes]
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train_crops, train_labels, test_crops, test_labels = [], [], [], []
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for label, indices in MILK_SHELF_LABELS.items():
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for position, index in enumerate(indices):
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if position < args.views:
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train_crops.append(crops[index])
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train_labels.append(label)
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else:
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test_crops.append(crops[index])
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test_labels.append(label)
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class_ids = sorted(MILK_SHELF_LABELS)
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print(f"{len(train_crops)} enrolment views, {len(test_crops)} held-out facings, {len(class_ids)} products")
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model, device = build_model()
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print(f"embedding {len(train_crops) * (args.augment + 1) + len(test_crops)} crops on {device}...")
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rng = np.random.RandomState(11)
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augmented, augmented_labels = [], []
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for crop, label in zip(train_crops, train_labels):
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augmented.append(crop)
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augmented_labels.append(label)
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for _ in range(args.augment):
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augmented.append(augment(crop, rng))
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augmented_labels.append(label)
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train_features = embed(model, device, augmented)
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test_features = embed(model, device, test_crops)
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def score(similarity, label, threshold=None):
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"""Top-1 over every held-out facing, with optional rejection.
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Balanced accuracy is reported alongside the raw rate because the shelf is
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unbalanced (MEGMILK has 34 facings, 酪農3.6 has two), and a system that only
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ever answers "MEGMILK" would look respectable on the raw number.
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"""
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per_class = {class_id: [0, 0] for class_id in class_ids}
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correct = rejected = 0
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for index, truth in enumerate(test_labels):
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best = int(similarity[index].argmax())
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if threshold is not None and similarity[index][best] < threshold:
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rejected += 1
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per_class[truth][1] += 1
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continue
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guess = class_ids[best]
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per_class[truth][1] += 1
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if guess == truth:
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correct += 1
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per_class[truth][0] += 1
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recalls = [found / total for found, total in per_class.values() if total]
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balanced = sum(recalls) / max(1, len(recalls))
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answered = len(test_labels) - rejected
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answered_accuracy = correct / max(1, answered)
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print(f" {label:<26} top-1 {correct:>3}/{len(test_labels)} = {100*correct//len(test_labels):>3}% "
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f"balanced {100*balanced:>3.0f}% answered {answered:>3} "
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f"({100*answered_accuracy:>3.0f}% of those right) rejected {rejected}")
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return per_class
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import torch
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def class_prototypes(features):
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prototypes = []
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for class_id in class_ids:
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members = [i for i, label in enumerate(train_labels) if label == class_id]
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mean = features[members].mean(axis=0)
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prototypes.append(mean / (np.linalg.norm(mean) + 1e-8))
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return np.stack(prototypes)
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# 1. raw features, matched the way the shipped gallery matches: best enrolment view
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raw_prototypes = class_prototypes(train_features)
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score(test_features @ raw_prototypes.T, "raw DINOv2 cosine")
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score(test_features @ raw_prototypes.T, "raw, rejecting below 0.55", threshold=0.55)
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# 2. the trained classifier's prototypes, with an angular margin
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projection, prototypes = train_head(train_features, augmented_labels, class_ids,
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args.epochs, device, margin=0.25)
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with torch.no_grad():
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projected_test = torch.nn.functional.normalize(
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projection(torch.from_numpy(test_features).float().to(device)), dim=1).cpu().numpy()
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similarity = projected_test @ prototypes.cpu().numpy().T
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score(similarity, "fine-tuned + margin")
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per_class = score(similarity, "fine-tuned, rejecting < 0.4", threshold=0.4)
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print(" per class, answered only:")
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for class_id in class_ids:
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found, total = per_class[class_id]
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if total:
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print(f" {class_id:<16} {found:>2}/{total:<3} {100*found//total:>4}%")
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print("\n note: enrolment and held-out facings come from one photograph, so these are")
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print(" upper bounds. A cross-visit number needs a second visit, not a second split.")
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if __name__ == "__main__":
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main()
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