transformers-knn-adapter 0.8.0

Hugging Face Transformers image embedding adapter with a scikit-learn KNN classification head.

transformers-knn-adapter

Hugging Face image embeddings with a scikit-learn KNN head.

transformers_knn_adapter extends Hugging Face image models by attaching a scikit-learn KNN classifier on top of transformer embeddings.

Requirements

• Python 3.11+
• uv

Setup

uv sync --dev

Run Tests

uv run pytest

Train

uv run python -m transformers_knn_adapter.knn_image_pipeline train \
  --model microsoft/resnet-50 \
  --knn-model-path /tmp/knn/dinov2_small_mini_imagenet_full.joblib \
  --dataset timm/mini-imagenet \
  --split train \
  --max-samples 1000 \
  --shuffle \
  --grid-search \
  --grid-search-splits 3 \
  --grid-search-repeats 2 \
  --grid-search-scoring f1_macro

Evaluate

uv run python -m transformers_knn_adapter.knn_image_pipeline eval \
  --model microsoft/resnet-50 \
  --knn-model-path /tmp/knn/dinov2_small_mini_imagenet_full.joblib \
  --dataset timm/mini-imagenet \
  --split test \
  --stratified \
  --max-samples 100 \
  --shuffle \
  --batch-size 100

Inference

uv run python -m transformers_knn_adapter.knn_image_pipeline infer \
  --model microsoft/resnet-50 \
  --knn-model-path /tmp/knn/dinov2_small_mini_imagenet_full.joblib \
  --image https://picsum.photos/200 \
  --inference-batch-size 5

CLI Arguments

train

Argument	Required	Default	Description
--model	Yes	-	HF model id/path for feature extraction
--knn-model-path	Yes	-	Path to save/load KNN model (.joblib)
--dataset	Yes	-	HF dataset name or local imagefolder path
--split	No	train	Dataset split
--image-column	No	image	Dataset image column
--label-column	No	label	Dataset label column
--batch-size	No	16	Embedding batch size
--stream	No	false	Enable streaming mode
--stratified	No	false	Stratified sampling (--max-samples subset size, non-streaming only)
--shuffle	No	false	Shuffle dataset before sampling/training
--shuffle-seed	No	42	Shuffle seed
--shuffle-buffer-size	No	1000	Streaming shuffle buffer size
--max-samples	No	None	Optional training sample cap
--n-neighbors	No	None	KNN neighbors (mutually exclusive with --grid-search)
--grid-search	No	false	Run GridSearchCV over neighbors/metrics
--grid-search-splits	No	None	Stratified splits per repeat for grid search
--grid-search-repeats	No	None	Repeat count for stratified folds in grid search
--grid-search-scoring	No	None	Grid-search scoring metric (f1_macro, precision_macro, recall_macro)
--top-k	No	2	Top-k at inference time
--device	No	-1	Transformers device index (-1 for CPU)

eval

Argument	Required	Default	Description
--model	Yes	-	HF model id/path for feature extraction
--knn-model-path	Yes	-	Path to trained KNN model (.joblib)
--dataset	Yes	-	HF dataset name or local imagefolder path
--split	No	validation	Dataset split
--image-column	No	image	Dataset image column
--label-column	No	label	Dataset label column
--batch-size	No	16	Embedding batch size
--stream	No	false	Enable streaming mode
--stratified	No	false	Stratified sampling (--max-samples subset size, non-streaming only)
--shuffle	No	false	Shuffle dataset before evaluation
--shuffle-seed	No	42	Shuffle seed
--shuffle-buffer-size	No	1000	Streaming shuffle buffer size
--max-samples	No	None	Optional evaluation sample cap
--min-class-instances	No	None	Drop classes with fewer than this number of eval instances
--negative-classes	No	other	Comma-separated classes treated as negative
--positive-classes-population-ratio	No	None	Target positive/(total) ratio after subsampling
--top-k	No	1	Top-k predictions (evaluation uses top-1)
--device	No	-1	Transformers device index (-1 for CPU)

infer

Argument	Required	Default	Description
--model	Yes	-	HF model id/path for feature extraction
--knn-model-path	Yes	-	Path to save/load KNN model (.joblib)
--top-k	No	3	Top-k predictions
--device	No	-1	Transformers device index (-1 for CPU)
--image	No	https://picsum.photos/200	Image input (file path or URL)
--inference-batch-size	No	5	Number of images for batched inference

predict

Argument	Required	Default	Description
--model	Yes	-	HF model id/path for feature extraction
--knn-model-path	Yes	-	Path to trained KNN model (.joblib)
--image	Yes	-	Image path/URL accepted by Transformers image pipeline
--top-k	No	3	Top-k predictions
--device	No	-1	Transformers device index (-1 for CPU)

Python API

from transformers_knn_adapter import pipeline

clf = pipeline(
    "image-classification",
    model_path="microsoft/resnet-50",
    knn_model_path="/tmp/knn/model.joblib",
)

Train from Python

clf.train(
    dataset="timm/mini-imagenet",
    split="train",
    max_samples=1000,
    shuffle=True,
    grid_search=True,
    grid_search_splits=3,
    grid_search_repeats=2,
    grid_search_scoring="f1_macro",
)

Evaluate from Python

metrics = clf.evaluate(
    dataset="timm/mini-imagenet",
    split="test",
    max_samples=100,
    shuffle=True,
    batch_size=100,
)
print(metrics["top1_accuracy"])

Inference from Python

single = clf("https://picsum.photos/200")
batch = clf(["https://picsum.photos/200"] * 5)
print(single)
print(batch)

Notes

Real train/eval runs can download model and dataset artifacts from Hugging Face.

Trainer Integration

The package also provides:

• Dinov2ForImageClassificationWithArcFaceLoss
• KNNCallback
• FreezeScheduleCallback

These are useful when you already have a working Hugging Face Trainer script for Dinov2ForImageClassification and want to:

• replace the classifier loss with ArcFace
• log KNN retrieval metrics during evaluation
• freeze and unfreeze modules by epoch

ArcFace Dinov2 Model

Dinov2ForImageClassificationWithArcFaceLoss is intended as a Trainer-compatible replacement for Dinov2ForImageClassification.

Example:

from transformers import AutoConfig, AutoImageProcessor, Trainer, TrainingArguments
from transformers_knn_adapter import Dinov2ForImageClassificationWithArcFaceLoss

config = AutoConfig.from_pretrained("facebook/dinov2-small")
config.num_labels = num_labels
config.label2id = label2id
config.id2label = id2label
config.arcface_margin = 28.6
config.arcface_scale = 64.0

image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-small")
model = Dinov2ForImageClassificationWithArcFaceLoss.from_pretrained(
    "facebook/dinov2-small",
    config=config,
    ignore_mismatched_sizes=True,
)

training_args = TrainingArguments(
    output_dir="/tmp/arcface-run",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    dataloader_num_workers=4,
    learning_rate=1e-3,
    report_to="none",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=data_collator,
    processing_class=image_processor,
    compute_metrics=compute_metrics,
)
trainer.train()

Notes:

• training loss uses ArcFace logits
• outputs.logits returned to Trainer evaluation/prediction are inference logits, not margin-modified training logits
• the ArcFace head weight matrix is initialized automatically when loading from a plain Dinov2 checkpoint

KNN Callback

KNNCallback evaluates embeddings with a scikit-learn KNN classifier during Trainer.evaluate().

Current embedding sources:

• cls
• cls_mean

Example:

from transformers_knn_adapter import KNNCallback

trainer.add_callback(
    KNNCallback(
        trainer=trainer,
        label_column="labels",
        ks=(1, 5),
        embedding_source="cls_mean",
    )
)

Requirements:

• train and eval datasets must yield pixel_values
• train and eval datasets must expose the label column you pass to label_column
• for ViT-like models, hidden states must be available so the callback can extract token embeddings

Current callback behavior:

• uses trainer.args.per_device_eval_batch_size by default
• uses trainer.args.dataloader_num_workers by default
• fits KNN once at max(ks) and derives smaller-k metrics from the same neighbor query

Combined Trainer Example

from transformers import AutoConfig, AutoImageProcessor, Trainer, TrainingArguments
from transformers_knn_adapter import (
    Dinov2ForImageClassificationWithArcFaceLoss,
    FreezeScheduleCallback,
    KNNCallback,
)

config = AutoConfig.from_pretrained("facebook/dinov2-small")
config.num_labels = num_labels
config.label2id = label2id
config.id2label = id2label
config.arcface_margin = 28.6
config.arcface_scale = 64.0

model = Dinov2ForImageClassificationWithArcFaceLoss.from_pretrained(
    "facebook/dinov2-small",
    config=config,
    ignore_mismatched_sizes=True,
)
image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-small")

training_args = TrainingArguments(
    output_dir="/tmp/arcface-run",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    dataloader_num_workers=4,
    learning_rate=1e-3,
    num_train_epochs=10,
    report_to="none",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=data_collator,
    processing_class=image_processor,
    compute_metrics=compute_metrics,
)

trainer.add_callback(
    FreezeScheduleCallback(
        trainer=trainer,
        freeze_schedule=[
            {"epoch": 0.0, "freeze_modules": ["dinov2"], "unfreeze_modules": []},
            {
                "epoch": 3.0,
                "freeze_modules": [],
                "unfreeze_modules": ["dinov2.encoder.layer.11"],
            },
        ],
    )
)
trainer.add_callback(
    KNNCallback(
        trainer=trainer,
        label_column="labels",
        ks=(1, 5),
        embedding_source="cls_mean",
    )
)

trainer.train()
metrics = trainer.evaluate()

Freeze Schedule Callback

FreezeScheduleCallback applies module freeze/unfreeze rules based on epoch number.

You can provide the schedule inline without modifying model.config:

from transformers_knn_adapter import FreezeScheduleCallback

freeze_schedule = [
    {"epoch": 0.0, "freeze_modules": ["dinov2"], "unfreeze_modules": []},
    {"epoch": 3.0, "freeze_modules": [], "unfreeze_modules": ["dinov2.encoder.layer.11"]},
]

trainer.add_callback(
    FreezeScheduleCallback(
        trainer=trainer,
        freeze_schedule=freeze_schedule,
    )
)

Or, if you prefer, store the schedule on model.config.freeze_schedule and instantiate the callback without the freeze_schedule= argument.

Schedule format:

[
    {
        "epoch": 0.0,
        "freeze_modules": ["dinov2"],
        "unfreeze_modules": [],
    },
    {
        "epoch": 3.0,
        "freeze_modules": [],
        "unfreeze_modules": ["dinov2.encoder.layer.11"],
    },
]

The callback:

• applies the schedule at train begin and each epoch begin
• logs trainable parameter count as:
  • train/trainable_parameters

Module names must match model.named_modules(), for example:

• dinov2
• dinov2.embeddings
• dinov2.encoder.layer.11
• . for the whole model

Smoke Script

The repository includes a practical end-to-end example in:

• scripts/dogfaces_smoke.py

It shows how to combine:

• Dinov2ForImageClassificationWithArcFaceLoss
• KNNCallback
• FreezeScheduleCallback
• Hugging Face Trainer
• optional W&B logging

Example:

uv run --with accelerate --with pytorch-metric-learning python scripts/dogfaces_smoke.py \
  --dataset dimidagd/DogFaceNet_224resize \
  --model facebook/dinov2-small \
  --freeze-schedule-config configs/freeze_schedule.backbone_then_unfreeze.json \
  --num-train-epochs 10 \
  --learning-rate 0.001 \
  --warmup-steps 10 \
  --logging-strategy steps \
  --logging-steps 20