source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/ | .md | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Unless required by applicable law or agr... | 198_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | The InstructBLIPVideo is an extension of the models proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. | 198_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | InstructBLIPVideo uses the same architecture as [InstructBLIP](instructblip) and works with the same checkpoints as [InstructBLIP](instructblip). The only difference is the ability to process videos.
The abstract from the paper is the following: | 198_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | *General-purpose language models that can solve various language-domain tasks have emerged driven by the pre-training and instruction-tuning pipeline. However, building general-purpose vision-language models is challenging due to the increased task discrepancy introduced by the additional visual input. Although vision-... | 198_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | instruction tuning remains relatively less explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pre-trained BLIP-2 models. We gather a wide variety of 26 publicly available datasets, transform them into instruction tuning format and categorize them ... | 198_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | zero-shot evaluation. Additionally, we introduce instruction-aware visual feature extraction, a crucial method that enables the model to extract informative features tailored to the given instruction. The resulting InstructBLIP models achieve state-of-the-art zero-shot performance across all 13 held-out datasets, subst... | 198_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA IMG). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models.* | 198_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | .md | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg"
alt="drawing" width="600"/>
<small> InstructBLIPVideo architecture. Taken from the <a href="https://arxiv.org/abs/2305.06500">original paper.</a> </small>
This model was cont... | 198_1_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#usage-tips | .md | - The model was trained by sampling 4 frames per video, so it's recommended to sample 4 frames
> [!NOTE] | 198_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#usage-tips | .md | > BLIP models after release v4.46 will raise warnings about adding `processor.num_query_tokens = {{num_query_tokens}}` and expand model embeddings layer to add special `<image>` token. It is strongly recommended to add the attributes to the processor if you own the model checkpoint, or open a PR if it is not owned by y... | 198_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#usage-tips | .md | of query tokens required per image and expand the text with as many `<image>` placeholders as there will be query tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there wil be failure when merging the embeddings. | 198_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#usage-tips | .md | The attributes can be obtained from model config, as `model.config.num_query_tokens` and model embeddings expansion can be done by following [this link](https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042). | 198_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | [`InstructBlipVideoConfig`] is the configuration class to store the configuration of a
[`InstructBlipVideoForConditionalGeneration`]. It is used to instantiate a Instructblipvideo model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configurat... | 198_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | the defaults will yield a similar configuration to that of the Instructblipvideo
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [... | 198_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVideoVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVideoQFormerConfig`].
text_config (`dict`, *optional*):
Dictionary of configuration opti... | 198_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | num_query_tokens (`int`, *optional*, defaults to 32):
The number of query tokens passed through the Transformer.
video_token_index (`int`, *optional*):
Token index of special video token.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... InstructBlipVi... | 198_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | >>> # Initializing a InstructBlipVideoConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoConfig()
>>> # Initializing a InstructBlipVideoForConditionalGeneration (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBli... | 198_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | >>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a InstructBlipVideoConfig from a InstructBlipVideoVisionConfig, InstructBlipVideoQFormerConfig and any PretrainedConfig
>>> # Initializing Instructblipvideo vision, Instructblipvideo Q-Former and language model confi... | 198_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | .md | >>> config = InstructBlipVideoConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
```
Methods: from_vision_qformer_text_configs | 198_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | This is the configuration class to store the configuration of a [`InstructBlipVideoVisionModel`]. It is used to
instantiate a InstructBlipVideo vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the I... | 198_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*,... | 198_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 39):
Number of hidden layers in the Transformer encoder.
num_a... | 198_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
Th... | 198_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. to 1e-5): The epsilon used by the layer
normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normali... | 198_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries and values in the self-a... | 198_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | .md | >>> # Initializing a InstructBlipVideoVisionConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoVisionConfig()
>>> # Initializing a InstructBlipVideoVisionModel (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlip... | 198_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | This is the configuration class to store the configuration of a [`InstructBlipVideoQFormerModel`]. It is used to
instantiate a InstructBlipVideo Querying Transformer (Q-Former) model according to the specified arguments, defining the
model architecture. Instantiating a configuration with the defaults will yield a simil... | 198_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | the InstructBlipVideo [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5)
architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
Read the documentation from [`PretrainedConfig`] for more information.
Note that [`Instr... | 198_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
n... | 198_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in th... | 198_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully... | 198_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024... | 198_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Token id used for padding sequences.
position_embedding_type (`st... | 198_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more inform... | 198_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
cross_attention_frequency (`int`, *optional*, defaults to 2):
The frequency of adding cross-attention to the Transformer layers.
encoder_hidden_size (`int`, *optional*, defaults to 1408):
The hidden size of the hidden states for... | 198_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | .md | >>> # Initializing a InstructBlipVideo Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoQFormerConfig()
>>> # Initializing a model (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVideoQFormerModel(configuration)
>>> # ... | 198_5_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoprocessor | .md | Constructs an InstructBLIPVideo processor which wraps a InstructBLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipVideoProcessor`] offers all the functionalities of [`InstructBlipVideoImageProcessor`] and [`AutoTokenizer`]. See the
docstring of [`~InstructBlipVideoProcessor.__call__... | 198_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoprocessor | .md | Args:
image_processor (`InstructBlipVideoImageProcessor`):
An instance of [`InstructBlipVideoImageProcessor`]. The image processor is a required input.
tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
qformer_tokenizer (`AutoTokenizer`):
An instance of ['PreTrained... | 198_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | .md | Constructs a InstructBLIPVideo image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"height": 384, ... | 198_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | .md | Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
overridden by the `resa... | 198_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | .md | Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
`do_rescale` parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
ov... | 198_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | .md | overridden by the `rescale_factor` parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
imag... | 198_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | .md | Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optiona... | 198_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | .md | Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_rgb (`bool`, *optional... | 198_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionmodel | .md | No docstring available for InstructBlipVideoVisionModel
Methods: forward | 198_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformermodel | .md | Querying Transformer (Q-Former), used in InstructBlipVideo. Slightly modified from BLIP-2 as it also takes the
instruction as input.
Methods: forward | 198_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoforconditionalgeneration | .md | InstructBlipVideo Model for generating text given an image and an optional text prompt. The model consists of a vision
encoder, Querying Transformer (Q-Former) and a language model.
One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
the prompt. Otherwis... | 198_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoforconditionalgeneration | .md | the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings... | 198_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoforconditionalgeneration | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`InstructBlipVideoConfig`]): Model configuratio... | 198_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoforconditionalgeneration | .md | Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- generate | 198_10_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 199_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 199_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#overview | .md | OWLv2 was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2 scales up [OWL-ViT](owlvit) using self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. This results in large ga... | 199_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#overview | .md | *Open-vocabulary object detection has benefited greatly from pretrained vision-language models, but is still limited by the amount of available detection training data. While detection training data can be expanded by using Web image-text pairs as weak supervision, this has not been done at scales comparable to image-l... | 199_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#overview | .md | which uses an existing detector to generate pseudo-box annotations on image-text pairs. Major challenges in scaling self-training are the choice of label space, pseudo-annotation filtering, and training efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which address these challenges. OWLv2 surpass... | 199_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#overview | .md | detectors already at comparable training scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples, yielding further large improvement: With an L/14 architecture, OWL-ST improves AP on LVIS rare classes, for which the model has seen no human box annotations, from 31.2% to 44.6% (43% relative improv... | 199_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#overview | .md | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/owlv2_overview.png"
alt="drawing" width="600"/>
<small> OWLv2 high-level overview. Taken from the <a href="https://arxiv.org/abs/2306.09683">original paper</a>. </small>
This model was contributed by [niel... | 199_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | OWLv2 is, just like its predecessor [OWL-ViT](owlvit), a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final... | 199_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box h... | 199_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | [`Owlv2ImageProcessor`] can be used to resize (or rescale) and normalize images for the model and [`CLIPTokenizer`] is used to encode the text. [`Owlv2Processor`] wraps [`Owlv2ImageProcessor`] and [`CLIPTokenizer`] into a single instance to both encode the text and prepare the images. The following example shows how to... | 199_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | >>> from transformers import Owlv2Processor, Owlv2ForObjectDetection
>>> processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") | 199_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text_labels = [["a photo of a cat", "a photo of a dog"]]
>>> inputs = processor(text=text_labels, images=image, return_tensors="pt")
>>> outputs = model(**inputs) | 199_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.tensor([(image.height, image.width)])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_grounded_object_detection(
... ... | 199_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | ... )
>>> # Retrieve predictions for the first image for the corresponding text queries
>>> result = results[0]
>>> boxes, scores, text_labels = result["boxes"], result["scores"], result["text_labels"]
>>> for box, score, text_label in zip(boxes, scores, text_labels):
... box = [round(i, 2) for i in box.tolist()]
.... | 199_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | .md | Detected a photo of a cat with confidence 0.614 at location [341.67, 23.39, 642.32, 371.35]
Detected a photo of a cat with confidence 0.665 at location [6.75, 51.96, 326.62, 473.13]
``` | 199_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#resources | .md | - A demo notebook on using OWLv2 for zero- and one-shot (image-guided) object detection can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/OWLv2).
- [Zero-shot object detection task guide](../tasks/zero_shot_object_detection)
<Tip> | 199_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#resources | .md | - [Zero-shot object detection task guide](../tasks/zero_shot_object_detection)
<Tip>
The architecture of OWLv2 is identical to [OWL-ViT](owlvit), however the object detection head now also includes an objectness classifier, which predicts the (query-agnostic) likelihood that a predicted box contains an object (as o... | 199_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#resources | .md | Usage of OWLv2 is identical to [OWL-ViT](owlvit) with a new, updated image processor ([`Owlv2ImageProcessor`]).
</Tip> | 199_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2config | .md | [`Owlv2Config`] is the configuration class to store the configuration of an [`Owlv2Model`]. It is used to
instantiate an OWLv2 model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the O... | 199_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2config | .md | [google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary ... | 199_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2config | .md | vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Owlv2VisionConfig`].
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_sc... | 199_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2config | .md | implementation.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not the model should return a dictionary. If `False`, returns a tuple.
kwargs (*optional*):
Dictionary of keyword arguments.
Methods: from_text_vision_configs | 199_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | This is the configuration class to store the configuration of an [`Owlv2TextModel`]. It is used to instantiate an
Owlv2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Owlv2
[google/owlv... | 199_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 49408):
Vocabulary size of the OWLv2 text model. Defines the number of different tokens that can... | 199_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | hidden_size (`int`, *optional*, defaults to 512):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number ... | 199_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 16):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
hidden_act (`str` or `function`... | 199_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, d... | 199_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should ... | 199_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | testing).
pad_token_id (`int`, *optional*, defaults to 0):
The id of the padding token in the input sequences.
bos_token_id (`int`, *optional*, defaults to 49406):
The id of the beginning-of-sequence token in the input sequences.
eos_token_id (`int`, *optional*, defaults to 49407):
The id of the end-of-sequence token i... | 199_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | .md | >>> # Initializing a Owlv2TextModel with google/owlv2-base-patch16 style configuration
>>> configuration = Owlv2TextConfig()
>>> # Initializing a Owlv2TextConfig from the google/owlv2-base-patch16 style configuration
>>> model = Owlv2TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration =... | 199_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | This is the configuration class to store the configuration of an [`Owlv2VisionModel`]. It is used to instantiate
an OWLv2 image encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the OWLv2
[google/o... | 199_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | [google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 7... | 199_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_a... | 199_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input images.
image_size (`int`, *optional*, defaults to 768):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resoluti... | 199_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, de... | 199_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.... | 199_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | testing).
Example:
```python
>>> from transformers import Owlv2VisionConfig, Owlv2VisionModel | 199_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | .md | >>> # Initializing a Owlv2VisionModel with google/owlv2-base-patch16 style configuration
>>> configuration = Owlv2VisionConfig()
>>> # Initializing a Owlv2VisionModel model from the google/owlv2-base-patch16 style configuration
>>> model = Owlv2VisionModel(configuration)
>>> # Accessing the model configuration
>>> co... | 199_6_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2imageprocessor | .md | Constructs an OWLv2 image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in
the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if ... | 199_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2imageprocessor | .md | method.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image to a square with gray pixels on the bottom and the right. Can be overriden by
`do_pad` in the `preprocess` method.
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to th... | 199_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2imageprocessor | .md | by `do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"height": 960, "width": 960}`):
Size to resize the image to. Can be overriden by `size` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling method to use if resi... | 199_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2imageprocessor | .md | do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats ... | 199_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2imageprocessor | .md | channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can... | 199_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2processor | .md | Owlv2Processor
Constructs an Owlv2 processor which wraps [`Owlv2ImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`] into
a single processor that interits both the image processor and tokenizer functionalities. See the
[`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
Args:... | 199_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2processor | .md | tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]):
The tokenizer is a required input.
- __call__
- post_process_grounded_object_detection
- post_process_image_guided_detection | 199_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2model | .md | This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | 199_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2model | .md | and behavior.
Parameters:
config ([`Owvl2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Method... | 199_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textmodel | .md | No docstring available for Owlv2TextModel
Methods: forward | 199_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionmodel | .md | No docstring available for Owlv2VisionModel
Methods: forward | 199_11_0 |
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