vit-zoo 0.1.1

Vision Transformers Zoo

Vision Transformer Zoo

A clean, extensible factory for creating HuggingFace-based Vision Transformer models (ViT, DeiT, DINO, DINOv2, DINOv3, CLIP) with flexible heads and easy backbone freezing.

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Installation (PyPI)

pip3 install vit_zoo

Installation (From source)

git clone [e-mail]:jbindaAI/vit_zoo.git
cd vit_zoo
pip install -e .

For development: pip install -e ".[dev]"

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Quick Start

Basic Usage

from vit_zoo.factory import build_model

# Simple classification
model = build_model("vanilla_vit", head=10, freeze_backbone=True)
predictions = model(images)  # Shape: (batch_size, 10)

Custom MLP Head

from vit_zoo.factory import build_model
from vit_zoo.components import MLPHead

mlp_head = MLPHead(
    input_dim=768,
    hidden_dims=[512, 256],
    output_dim=100,
    dropout=0.1,
    activation="gelu"  # or 'relu', 'tanh', or nn.Module
)

model = build_model("dinov2_vit", head=mlp_head)

Embedding Extraction

model = build_model("clip_vit", head=None)
outputs = model(images, output_embeddings=True)
embeddings = outputs["embeddings"]  # Shape: (batch_size, embedding_dim)

Attention Weights

model = build_model(
    "vanilla_vit",
    head=10,
    config_kwargs={"attn_implementation": "eager"}
)
outputs = model(images, output_attentions=True)
attentions = outputs["attentions"]

Custom Head

from vit_zoo.components import BaseHead
import torch.nn as nn

class CustomHead(BaseHead):
    def __init__(self, input_dim: int, num_classes: int):
        super().__init__()
        self._input_dim = input_dim
        self.fc = nn.Linear(input_dim, num_classes)
    
    @property
    def input_dim(self) -> int:
        return self._input_dim
    
    def forward(self, embeddings):
        return self.fc(embeddings)

head = CustomHead(input_dim=768, num_classes=10)
model = build_model("vanilla_vit", head=head)

Direct Usage (Any HuggingFace Model)

from vit_zoo.factory import build_model
from transformers import ViTModel

model = build_model(
    model_name="google/vit-large-patch16-224",
    backbone_cls=ViTModel,
    head=10
)

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API Reference

build_model()

build_model(
    model_type: Optional[str] = None,
    model_name: Optional[str] = None,
    backbone_cls: Optional[Type[ViTBackboneProtocol]] = None,
    head: Optional[Union[int, BaseHead]] = None,
    freeze_backbone: bool = False,
    load_pretrained: bool = True,
    backbone_dropout: float = 0.0,
    config_kwargs: Optional[Dict[str, Any]] = None,
) -> ViTModel

Parameters:

• model_type: Registry key ("vanilla_vit", "deit_vit", "dinov2_vit", etc.)
• head: int (creates LinearHead), BaseHead instance, or None (embedding extraction)
• freeze_backbone: Freeze all backbone parameters
• config_kwargs: Extra config options (e.g., {"attn_implementation": "eager"})

Usage:

• Registry: build_model("vanilla_vit", head=10)
• Override: build_model("vanilla_vit", model_name="google/vit-large-patch16-224", head=10)
• Direct: build_model(model_name="...", backbone_cls=ViTModel, head=10)

ViTModel.forward()

forward(
    pixel_values: torch.Tensor,
    output_attentions: bool = False,
    output_embeddings: bool = False,
) -> Union[torch.Tensor, Dict[str, Any]]

Returns predictions tensor, or dict with "predictions", "attentions", "embeddings" keys.

ViTModel.freeze_backbone()

model.freeze_backbone(freeze: bool = True)  # Freeze/unfreeze backbone

list_models()

from vit_zoo.factory import list_models
available = list_models()  # Returns list of registered model types

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Supported Models

• vanilla_vit: Google ViT (google/vit-base-patch16-224)
• deit_vit: Facebook DeiT (facebook/deit-base-distilled-patch16-224)
• dino_vit: Facebook DINO (facebook/dino-vitb16)
• dinov2_vit: Facebook DINOv2 (facebook/dinov2-base)
• dinov2_reg_vit: DINOv2 with registers (facebook/dinov2-with-registers-base)
• dinov3_vit: Facebook DINOv3 (facebook/dinov3-vitb16-pretrain-lvd1689m)
• clip_vit: OpenAI CLIP Vision (openai/clip-vit-base-patch16)

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Import Patterns

from vit_zoo import ViTModel
from vit_zoo.factory import build_model, list_models
from vit_zoo.components import ViTBackbone, BaseHead, LinearHead, MLPHead, IdentityHead

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Available Heads

• LinearHead: Simple linear layer (auto-created when head=int)
• MLPHead: Multi-layer perceptron with configurable depth, activation, dropout
• IdentityHead: Returns embeddings unchanged

All heads must implement input_dim property. Custom heads by subclassing BaseHead.

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License

GPL-3.0