+ j^RIHt^RIHtHtHt^RIHtHtH t H t H t ^RI H t Ht] !4'd ^RIHt^RIHt]!4'd ^RIt^RIHt] P,!]4t]!]!R R 74!R R ] 44tR#) )UserDict)AnyUnionoverload)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_imageN)6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMEST)has_image_processorcaa]tRt^toRtRtRtRtRtV3Rlt ] V3RlRl4t ] V3RlRl4t V3R lV3R llt RR lt RR lt R tRtRtVtV;t#)#ZeroShotImageClassificationPipelinea Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you provide an image and a set of `candidate_labels`. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="google/siglip-so400m-patch14-384") >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["animals", "humans", "landscape"], ... ) [{'score': 0.965, 'label': 'animals'}, {'score': 0.03, 'label': 'humans'}, {'score': 0.005, 'label': 'landscape'}] >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["black and white", "photorealist", "painting"], ... ) [{'score': 0.996, 'label': 'black and white'}, {'score': 0.003, 'label': 'photorealist'}, {'score': 0.0, 'label': 'painting'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-image-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-image-classification). FTc j<\SV`!R/VB\VR4VP\4R#)visionN)super__init__r check_model_typer)selfkwargs __class__s&,v/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/pipelines/zero_shot_image_classification.pyr,ZeroShotImageClassificationPipeline.__init__Cs, "6"$) TUc |<V^8dQhRS[S[R3,RS[S[,RS[RS[S[S[S[3,,/#imagez Image.Imagecandidate_labelsrreturnrstrlistrdict)format __classdict__s"r __annotate__0ZeroShotImageClassificationPipeline.__annotate__JsH##3 -.#BFs)#WZ# d38n #rc R#Nrrr#r$rs&&&,r__call__,ZeroShotImageClassificationPipeline.__call__Is #rc <V^8dQhRS[S[,S[R,,RS[S[,RS[RS[S[S[S[S[3,,,/#r!)r(r'rr))r*r+s"rr,r-OsQ))#Ym!44)HLS )]`) d4S>" #)rc R#r/rr0s&&&,rr1r2Ns&)rc <V^8dQhRS[S[S[S[,RS[R,3,RS[S[,RS[RS[S[S[S[3,,S[S[S[S[S[3,,,,/#r!r&)r*r+s"rr,r-Ssx)T)TS$s)]D4GGH)Ts))T )T d38n T$sCx.%9 : : )Trc x<RV9dVPR4pVf \R4h\SV` !V3RV/VB#)a; Assign labels to the image(s) passed as inputs. Args: image (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly candidate_labels (`list[str]`): The candidate labels for this image. They will be formatted using *hypothesis_template*. hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`): The format used in conjunction with *candidate_labels* to attempt the image classification by replacing the placeholder with the candidate_labels. Pass "{}" if *candidate_labels* are already formatted. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A list of dictionaries containing one entry per proposed label. Each dictionary contains the following keys: - **label** (`str`) -- One of the suggested *candidate_labels*. - **score** (`float`) -- The score attributed by the model to that label. 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