docs/source/en/tasks/training_vision_backbone.md

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# Training Vision Models using Backbone API

Computer vision workflows follow a common pattern. Use a pre-trained backbone for feature extraction ([ViT](../model_doc/vit), [DINOv3](../model_doc/dinov3)). Add a "neck" for feature enhancement. Attach a task-specific head ([DETR](../model_doc/detr) for object detection, [MaskFormer](../model_doc/maskformer) for segmentation).

The Transformers library implements these models and the [backbone API](../backbones) lets you swap different backbones and heads with minimal code.

![Backbone Explanation](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Backbone.png)

This guide combines [DINOv3 with ConvNext architecture](https://huggingface.co/facebook/dinov3-convnext-large-pretrain-lvd1689m) and a [DETR head](https://huggingface.co/facebook/detr-resnet-50). You'll train on the [license plate detection dataset](https://huggingface.co/datasets/merve/license-plates). DINOv3 delivers the best performance as of this writing.

> [!NOTE]
> This model requires access approval. Visit [the model repository](https://huggingface.co/facebook/dinov3-convnext-large-pretrain-lvd1689m) to request access.

Install [trackio](https://github.com/gradio-app/trackio) for experiment tracking and [albumentations](https://albumentations.ai/) for data augmentation. Use the latest transformers version.

```bash
pip install -Uq albumentations trackio transformers datasets
```

Initialize [`DetrConfig`] with the pre-trained DINOv3 ConvNext backbone. Use `num_labels=1` to detect the license plate bounding boxes. Create [`DetrForObjectDetection`] with this configuration. Freeze the backbone to preserve DINOv3 features without updating weights. Load the [`DetrImageProcessor`].

```py
from transformers import DetrConfig, DetrForObjectDetection, AutoImageProcessor

config = DetrConfig(backbone="facebook/dinov3-convnext-large-pretrain-lvd1689m",
                    use_pretrained_backbone=True, use_timm_backbone=False,
                    num_labels=1, id2label={0: "license_plate"}, label2id={"license_plate": 0})
model = DetrForObjectDetection(config)

for param in model.model.backbone.parameters():
    param.requires_grad = False
image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
```

Load the dataset and split it for training.

```py
from datasets import load_dataset
ds = load_dataset("merve/license-plates")
ds = ds["train"]

ds = ds.train_test_split(test_size=0.05)
train_dataset = ds["train"]
val_dataset = ds["test"]
len(train_dataset)
# 5867
```

Augment the dataset. Rescale images to a maximum size, flip them, and apply affine transforms. Eliminate invalid bounding boxes and ensure annotations stay clean with `rebuild_objects`.

```py
import albumentations as A
import numpy as np
from PIL import Image

train_aug = A.Compose(
    [
        A.LongestMaxSize(max_size=1024, p=1.0),
        A.HorizontalFlip(p=0.5),
        A.Affine(rotate=(-5, 5), shear=(-5, 5), translate_percent=(0.05, 0.05), p=0.5),
    ],
    bbox_params=A.BboxParams(format="coco", label_fields=["category_id"], min_visibility=0.0),
)

def train_transform(batch):
    imgs_out, objs_out = [], []
    original_imgs, original_objs = batch["image"], batch["objects"]

    for i, (img_pil, objs) in enumerate(zip(original_imgs, original_objs)):
        img = np.array(img_pil)
        labels = [0] * len(objs["bbox"])

        out = train_aug(image=img, bboxes=list(objs["bbox"]), category_id=labels)

        if len(out["bboxes"]) == 0:
            imgs_out.append(img_pil) # if no boxes left after augmentation, use original
            objs_out.append(objs)
            continue

        H, W = out["image"].shape[:2]
        clamped = []
        for (x, y, w, h) in out["bboxes"]:
            x = max(0.0, min(x, W - 1.0))
            y = max(0.0, min(y, H - 1.0))
            w = max(1.0, min(w, W - x))
            h = max(1.0, min(h, H - y))
            clamped.append([x, y, w, h])

        imgs_out.append(Image.fromarray(out["image"]))
        objs_out.append(rebuild_objects(clamped, out["category_id"]))

    batch["image"] = imgs_out
    batch["objects"] = objs_out
    return batch



def rebuild_objects(bboxes, labels):
    bboxes = [list(map(float, b)) for b in bboxes]
    areas  = [float(w*h) for (_, _, w, h) in bboxes]
    ids    = list(range(len(bboxes)))
    return {
        "id": ids,
        "bbox": bboxes,
        "category_id": list(map(int, labels)),
        "area": areas,
        "iscrowd": [0]*len(bboxes),
    }

train_dataset = train_dataset.with_transform(train_transform)
```

Build COCO-style annotations for the image processor.

```py
import torch

def format_annotations(image, objects, image_id):
    n = len(objects["id"])
    anns = []
    iscrowd_list = objects.get("iscrowd", [0] * n)
    area_list = objects.get("area", None)

    for i in range(n):
        x, y, w, h = objects["bbox"][i]
        area = area_list[i] if area_list is not None else float(w * h)

        anns.append({
            "id": int(objects["id"][i]),
            "iscrowd": int(iscrowd_list[i]),
            "bbox": [float(x), float(y), float(w), float(h)],
            "category_id": int(objects.get("category_id", objects.get("category"))[i]),
            "area": float(area),
        })

    return {"image_id": int(image_id), "annotations": anns}
```

Create batches in the data collator. Format annotations and pass them with transformed images to the image processor.

```py
def collate_fn(examples):
    images = [example["image"] for example in examples]
    ann_batch = [format_annotations(example["image"], example["objects"], example["image_id"]) for example in examples]

    inputs = image_processor(images=images, annotations=ann_batch, return_tensors="pt")
    return inputs
```

Initialize the [`Trainer`] and set up [`TrainingArguments`] for model convergence. Pass datasets, data collator, arguments, and model to `Trainer` to start training.

```py
from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./license-plate-detr-dinov3",
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    num_train_epochs=8,
    learning_rate=1e-5,
    weight_decay=1e-4,
    warmup_steps=500,
    eval_strategy="steps",
    eval_steps=500,
    save_total_limit=2,
    dataloader_pin_memory=False,
    fp16=True,
    report_to="trackio",
    load_best_model_at_end=True,
    remove_unused_columns=False,
    push_to_hub=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    data_collator=collate_fn,
)

trainer.train()
```

Push the trainer and image processor to the Hub.

```py
trainer.push_to_hub()
image_processor.push_to_hub("merve/license-plate-detr-dinov3")
```

Test the model with an object detection pipeline.

```py
from transformers import pipeline

obj_detector = pipeline(
    "object-detection", model="merve/license-plate-detr-dinov3"
)
results = obj_detector("https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/license-plates.jpg", threshold=0.05)
print(results)
```

Visualize the results.

```py
from PIL import Image, ImageDraw
import numpy as np
import requests


def plot_results(image, results, threshold):
    image = Image.fromarray(np.uint8(image))
    draw = ImageDraw.Draw(image)
    width, height = image.size

    for result in results:
        score = result["score"]
        label = result["label"]
        box = list(result["box"].values())

        if score > threshold:
            x1, y1, x2, y2 = tuple(box)
            draw.rectangle((x1, y1, x2, y2), outline="red")
            draw.text((x1 + 5, y1 + 10), f"{score:.2f}", fill="green" if score > 0.7 else "red")

    return image

image = Image.open(requests.get("https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/license-plates.jpg", stream=True).raw)
plot_results(image, results, threshold=0.05)
```

![Results](https://huggingface.co/datasets/huggingface/documentation-images/results/main/transformers/tasks/backbone_training_results.png)