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/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumconfig | .md | Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 100000.0):
The base period of the RoPE e... | 368_9_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumconfig | .md | Padding token id.
eos_token_id (`int` | `list`, *optional*, defaults to 2):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during ... | 368_9_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumconfig | .md | Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
```python
>>> from transformers import HeliumModel, HeliumConfig
>>> # Initializing a Helium 2b style configuration
>>> configuration = HeliumConfig()
>>> # Initializing a model from the Helium 2b style configuration
>>> model = HeliumM... | 368_9_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliummodel | .md | HeliumModel
The bare Helium Model outputting raw hidden-states without any specific head on top.
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
... | 368_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliummodel | .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 ([`HeliumConfig`]):
Model configuration class wit... | 368_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliummodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HeliumDecoderLayer`]
Args:
config: HeliumConfig
- forward | 368_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumforcausallm | .md | HeliumForCausalLM
- forward | 368_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumforsequenceclassification | .md | HeliumForSequenceClassification
The Helium Model transformer with a sequence classification head on top (linear layer).
[`HeliumForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to ... | 368_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumforsequenceclassification | .md | `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (ta... | 368_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumforsequenceclassification | .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... | 368_12_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumforsequenceclassification | .md | and behavior.
Parameters:
config ([`HeliumConfig`]):
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.
- for... | 368_12_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumfortokenclassification | .md | HeliumForTokenClassification
The Helium Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library im... | 368_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumfortokenclassification | .md | 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#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all ma... | 368_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/helium.md | https://huggingface.co/docs/transformers/en/model_doc/helium/#heliumfortokenclassification | .md | 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.
- forward | 368_13_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/ | .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... | 369_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/ | .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 ... | 369_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#overview | .md | The Autoformer model was proposed in [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
This model augments the Transformer as a deep decomposition architecture, which can progressively... | 369_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#overview | .md | *Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the long-term forecasting problem of time series. Prior Transformer-based models adopt various self-attention mechanisms to discover the long-ra... | 369_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#overview | .md | long-term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point-wise self-attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architectur... | 369_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#overview | .md | pre-processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series per... | 369_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#overview | .md | aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and diseas... | 369_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#overview | .md | This model was contributed by [elisim](https://huggingface.co/elisim) and [kashif](https://huggingface.co/kashif).
The original code can be found [here](https://github.com/thuml/Autoformer). | 369_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#resources | .md | A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing reso... | 369_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | AutoformerConfig
This is the configuration class to store the configuration of an [`AutoformerModel`]. It is used to instantiate an
Autoformer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the ... | 369_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | [huggingface/autoformer-tourism-monthly](https://huggingface.co/huggingface/autoformer-tourism-monthly)
architecture.
Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
prediction_length (`int... | 369_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | context_length (`int`, *optional*, defaults to `prediction_length`):
The context length for the encoder. If unset, the context length will be the same as the
`prediction_length`.
distribution_output (`string`, *optional*, defaults to `"student_t"`):
The distribution emission head for the model. Could be either "student... | 369_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | The loss function for the model corresponding to the `distribution_output` head. For parametric
distributions it is the negative log likelihood (nll) - which currently is the only supported one.
input_size (`int`, *optional*, defaults to 1):
The size of the target variable which by default is 1 for univariate targets. ... | 369_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | multivariate targets.
lags_sequence (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 5, 6, 7]`):
The lags of the input time series as covariates often dictated by the frequency. Default is `[1, 2, 3, 4,
5, 6, 7]`.
scaling (`bool`, *optional* defaults to `True`):
Whether to scale the input targets.
num_time_features ... | 369_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | The number of time features in the input time series.
num_dynamic_real_features (`int`, *optional*, defaults to 0):
The number of dynamic real valued features.
num_static_categorical_features (`int`, *optional*, defaults to 0):
The number of static categorical features.
num_static_real_features (`int`, *optional*, defa... | 369_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | The number of static real valued features.
cardinality (`list[int]`, *optional*):
The cardinality (number of different values) for each of the static categorical features. Should be a list
of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if
`num_static_categorical_features` is ... | 369_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | The dimension of the embedding for each of the static categorical features. Should be a list of integers,
having the same length as `num_static_categorical_features`. Cannot be `None` if
`num_static_categorical_features` is > 0.
d_model (`int`, *optional*, defaults to 64):
Dimensionality of the transformer layers.
enco... | 369_3_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 2):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 2):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 2):
Number of attentio... | 369_3_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | Dimension of the "intermediate" (often named feed-forward) layer in encoder.
decoder_ffn_dim (`int`, *optional*, defaults to 32):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (... | 369_3_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | `"relu"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the encoder, and decoder.
encoder_layerdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention and fully connected layers for each encoder layer.
decoder_layer... | 369_3_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | The dropout probability for the attention and fully connected layers for each decoder layer.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability used between the two layers o... | 369_3_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | The number of samples to generate in parallel for each time step of inference.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated normal weight initialization distribution.
use_cache (`bool`, *optional*, defaults to `True`):
Whether to use the past key/values attentions (if applic... | 369_3_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | Start token length of the Autoformer decoder, which is used for direct multi-step prediction (i.e.
non-autoregressive generation).
moving_average (`int`, *optional*, defaults to 25):
The window size of the moving average. In practice, it's the kernel size in AvgPool1d of the Decomposition
Layer.
autocorrelation_factor ... | 369_3_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | "Attention" (i.e. AutoCorrelation mechanism) factor which is used to find top k autocorrelations delays.
It's recommended in the paper to set it to a number between 1 and 5.
Example:
```python
>>> from transformers import AutoformerConfig, AutoformerModel | 369_3_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | .md | >>> # Initializing a default Autoformer configuration
>>> configuration = AutoformerConfig()
>>> # Randomly initializing a model (with random weights) from the configuration
>>> model = AutoformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 369_3_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformermodel | .md | AutoformerModel
The bare Autoformer Model outputting raw hidden-states without any specific head on top.
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, prunin... | 369_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformermodel | .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 ([`AutoformerConfig`]):
Model configuration class... | 369_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformermodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 369_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerforprediction | .md | AutoformerForPrediction
The Autoformer Model with a distribution head on top for time-series forecasting.
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, pruni... | 369_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerforprediction | .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 ([`AutoformerConfig`]):
Model configuration class... | 369_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerforprediction | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 369_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/ | .md | <!--Copyright 2022 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... | 370_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/ | .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 ... | 370_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | .md | The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke
and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero-shot and one-shot image segmentation.
The abstract from the paper is the following: ... | 370_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | .md | model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive
as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system
that can generate image segmentations based on arbitrary
prompts at test time. A prompt can ... | 370_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | .md | image. This approach enables us to create a unified model
(trained once) for three common segmentation tasks, which
come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation.
We build upon the CLIP model as a backbone which we extend with a transformer-based deco... | 370_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | .md | prediction. After training on an extended version of the
PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on
an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail.
This novel hybrid input allows fo... | 370_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | .md | only to the three segmentation tasks mentioned above, but
to any binary segmentation task where a text or image query
can be formulated. Finally, we find our system to adapt well
to generalized queries involving affordances or properties*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/reso... | 370_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | .md | alt="drawing" width="600"/>
<small> CLIPSeg overview. Taken from the <a href="https://arxiv.org/abs/2112.10003">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/timojl/clipseg). | 370_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#usage-tips | .md | - [`CLIPSegForImageSegmentation`] adds a decoder on top of [`CLIPSegModel`]. The latter is identical to [`CLIPModel`].
- [`CLIPSegForImageSegmentation`] can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text
(provided to the model as `input_ids`) or an image (provided to... | 370_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#resources | .md | A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIPSeg. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an ... | 370_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#resources | .md | <PipelineTag pipeline="image-segmentation"/>
- A notebook that illustrates [zero-shot image segmentation with CLIPSeg](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/CLIPSeg/Zero_shot_image_segmentation_with_CLIPSeg.ipynb). | 370_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | [`CLIPSegConfig`] is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to
instantiate a CLIPSeg 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 t... | 370_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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 of configurati... | 370_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`CLIPSegVisionConfig`].
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_... | 370_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | extract_layers (`List[int]`, *optional*, defaults to `[3, 6, 9]`):
Layers to extract when forwarding the query image through the frozen visual backbone of CLIP.
reduce_dim (`int`, *optional*, defaults to 64):
Dimensionality to reduce the CLIP vision embedding.
decoder_num_attention_heads (`int`, *optional*, defaults to... | 370_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | decoder_attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
decoder_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"... | 370_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | decoder_intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layers in the Transformer decoder.
conditional_layer (`int`, *optional*, defaults to 0):
The layer to use of the Transformer encoder whose activations will be combined with the condition
embeddings... | 370_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | use_complex_transposed_convolution (`bool`, *optional*, defaults to `False`):
Whether to use a more complex transposed convolution in the decoder, enabling more fine-grained
segmentation.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import CLIPSegConfig, CLIPSegMode... | 370_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | >>> # Initializing a CLIPSegConfig with CIDAS/clipseg-rd64 style configuration
>>> configuration = CLIPSegConfig()
>>> # Initializing a CLIPSegModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
>>> model = CLIPSegModel(configuration)
>>> # Accessing the model configuration
>>> configuration =... | 370_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | .md | >>> # We can also initialize a CLIPSegConfig from a CLIPSegTextConfig and a CLIPSegVisionConfig
>>> # Initializing a CLIPSegText and CLIPSegVision configuration
>>> config_text = CLIPSegTextConfig()
>>> config_vision = CLIPSegVisionConfig()
>>> config = CLIPSegConfig.from_text_vision_configs(config_text, config_visio... | 370_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .md | This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an
CLIPSeg model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the CLIPSeg
[CIDAS/clipseg-rd6... | 370_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .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 CLIPSeg text model. Defines the number of different tokens that c... | 370_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .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 ... | 370_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .md | Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 77):
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`... | 370_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .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... | 370_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .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 ... | 370_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .md | pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 49406):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 49407):
End of stream token id.
Example:
```python
>>> from transformers import CLIPSegTextConfig, CLIPSegTextModel | 370_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | .md | >>> # Initializing a CLIPSegTextConfig with CIDAS/clipseg-rd64 style configuration
>>> configuration = CLIPSegTextConfig()
>>> # Initializing a CLIPSegTextModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
>>> model = CLIPSegTextModel(configuration)
>>> # Accessing the model configuration
>>>... | 370_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an
CLIPSeg model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the CLIPSeg
[CIDAS/clipseg-rd6... | 370_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | 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 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *opt... | 370_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | num_hidden_layers (`int`, *optional*, defaults to 12):
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.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels... | 370_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 32):
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"` a... | 370_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
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_nor... | 370_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import CLIPSegVisionConfig, CLIPSegVisionModel | 370_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | .md | >>> # Initializing a CLIPSegVisionConfig with CIDAS/clipseg-rd64 style configuration
>>> configuration = CLIPSegVisionConfig()
>>> # Initializing a CLIPSegVisionModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
>>> model = CLIPSegVisionModel(configuration)
>>> # Accessing the model configura... | 370_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegprocessor | .md | Constructs a CLIPSeg processor which wraps a CLIPSeg image processor and a CLIP tokenizer into a single processor.
[`CLIPSegProcessor`] offers all the functionalities of [`ViTImageProcessor`] and [`CLIPTokenizerFast`]. See the
[`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.
Arg... | 370_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegmodel | .md | This model is 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 ([`CLIPSegConfig`]): Model configuration class with all the pa... | 370_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegmodel | .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
- get_text_features
- get_image_features | 370_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextmodel | .md | No docstring available for CLIPSegTextModel
Methods: forward | 370_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionmodel | .md | No docstring available for CLIPSegVisionModel
Methods: forward | 370_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegforimagesegmentation | .md | CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
This model is 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 ... | 370_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegforimagesegmentation | .md | behavior.
Parameters:
config ([`CLIPSegConfig`]): 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.
Methods:... | 370_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/ | .md | <!--Copyright 2022 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... | 371_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/ | .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 ... | 371_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | .md | The Conditional DETR model was proposed in [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Cond... | 371_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | .md | *The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motiv... | 371_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | .md | embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is... | 371_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | .md | head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the trainin... | 371_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | .md | 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.* | 371_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | .md | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg"
alt="drawing" width="600"/>
<small> Conditional DETR shows much faster convergence compared to the original DETR. Taken from the <a href="https://arxiv.org/abs/2108.06152">origina... | 371_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#resources | .md | - Scripts for finetuning [`ConditionalDetrForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
- See also: [Object detection task guide](../tasks/object_detection)... | 371_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrconfig | .md | This is the configuration class to store the configuration of a [`ConditionalDetrModel`]. It is used to instantiate
a Conditional DETR model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Conditiona... | 371_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrconfig | .md | [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_timm_backbone (`boo... | 371_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrconfig | .md | Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
API.
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
case it will default to `ResNetConfig()`.... | 371_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrconfig | .md | The number of input channels.
num_queries (`int`, *optional*, defaults to 100):
Number of object queries, i.e. detection slots. This is the maximal number of objects
[`ConditionalDetrModel`] can detect in a single image. For COCO, we recommend 100 queries.
d_model (`int`, *optional*, defaults to 256):
Dimension of the ... | 371_3_3 |
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