---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
## Code to create model
```python
import torch
from transformers import GroundingDinoConfig, GroundingDinoForObjectDetection, AutoProcessor

model_id = 'IDEA-Research/grounding-dino-tiny'
config = GroundingDinoConfig.from_pretrained(
    model_id,
    decoder_layers=1,
    decoder_attention_heads=2,
    encoder_layers=1,
    encoder_attention_heads=2,
    text_config=dict(
        num_attention_heads=2,
        num_hidden_layers=1,
        hidden_size=32,
    ),
    backbone_config=dict(
        attention_probs_dropout_prob=0.0,
        depths=[1, 1, 2, 1],
        drop_path_rate=0.1,
        embed_dim=12,
        encoder_stride=32,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        hidden_size=48,
        image_size=224,
        initializer_range=0.02,
        layer_norm_eps=1e-05,
        mlp_ratio=4.0,
        num_channels=3,
        num_heads=[1, 2, 3, 4],
        num_layers=4,
        out_features=["stage2", "stage3", "stage4"],
        out_indices=[2, 3, 4],
        patch_size=4,
        stage_names=["stem", "stage1", "stage2", "stage3", "stage4"],
        window_size=7
    )
)

# Create model and randomize all weights
model = GroundingDinoForObjectDetection(config)

torch.manual_seed(0) # Set for reproducibility
for name, param in model.named_parameters():
    param.data = torch.randn_like(param)

processor = AutoProcessor.from_pretrained(model_id)

print(model.num_parameters())  # 7751525
```


## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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#### Training Hyperparameters

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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