Zero-shot image classification task guide (#22132)

* WIP

* WIP

* manual inference example

* make style

* Apply suggestions from code review
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>

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Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>

docs/source/en/_toctree.yml

@@ -83,6 +83,8 @@
       title: Object detection
     - local: tasks/zero_shot_object_detection
       title: Zero-shot object detection
+    - local: tasks/zero_shot_image_classification
+      title: Zero-shot image classification
     title: Computer Vision
   - sections:
     - local: tasks/image_captioning

docs/source/en/tasks/zero_shot_image_classification.mdx

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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+-->
+
+# Zero-shot image classification
+
+[[open-in-colab]]
+
+Zero-shot image classification is a task that involves classifying images into different categories using a model that was
+not explicitly trained on data containing labeled examples from those specific categories.
+
+Traditionally, image classification requires training a model on a specific set of labeled images, and this model learns to
+"map" certain image features to labels. When there's a need to use such model for a classification task that introduces a
+new set of labels, fine-tuning is required to "recalibrate" the model.
+
+In contrast, zero-shot or open vocabulary image classification models are typically multi-modal models that have been trained on a large
+dataset of images and associated descriptions. These models learn aligned vision-language representations that can be used for many downstream tasks including zero-shot image classification.
+
+This is a more flexible approach to image classification that allows models to generalize to new and unseen categories
+without the need for additional training data and enables users to query images with free-form text descriptions of their target objects .
+
+In this guide you'll learn how to:
+
+* create a zero-shot image classification pipeline
+* run zero-shot image classification inference by hand
+
+Before you begin, make sure you have all the necessary libraries installed:
+
+```bash
+pip install -q transformers
+```
+
+## Zero-shot image classification pipeline
+
+The simplest way to try out inference with a model supporting zero-shot image classification is to use the corresponding [`pipeline`].
+Instantiate a pipeline from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads):
+
+```python
+>>> from transformers import pipeline
+
+>>> checkpoint = "openai/clip-vit-large-patch14"
+>>> detector = pipeline(model=checkpoint, task="zero-shot-image-classification")
+```
+
+Next, choose an image you'd like to classify.
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/owl.jpg" alt="Photo of an owl"/>
+</div>
+
+Pass the image and the candidate object labels to the pipeline. Here we pass the image directly; other suitable options
+include a local path to an image or an image url.
+The candidate labels can be simple words like in this example, or more descriptive.
+
+```py
+>>> predictions = classifier(image, candidate_labels=["fox", "bear", "seagull", "owl"])
+>>> predictions
+[{'score': 0.9996670484542847, 'label': 'owl'},
+ {'score': 0.000199399160919711, 'label': 'seagull'},
+ {'score': 7.392891711788252e-05, 'label': 'fox'},
+ {'score': 5.96074532950297e-05, 'label': 'bear'}]
+```
+
+## Zero-shot image classification by hand
+
+Now that you've seen how to use the zero-shot image classification pipeline, let's take a look how you can run zero-shot
+image classification manually.
+
+Start by loading the model and associated processor from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads).
+Here we'll use the same checkpoint as before:
+
+```py
+>>> from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
+
+>>> model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint)
+>>> processor = AutoProcessor.from_pretrained(checkpoint)
+```
+
+Let's take a different image to switch things up.
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/xBRQfR2bqNI/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjc4Mzg4ODEx&force=true&w=640"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg" alt="Photo of a car"/>
+</div>
+
+Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the
+image for the model by resizing and normalizing it, and a tokenizer that takes care of the text inputs.
+
+```py
+>>> candidate_labels = ["tree", "car", "bike", "cat"]
+>>> inputs = processor(images=image, text=candidate_labels, return_tensors="pt", padding=True)
+```
+
+Pass the inputs through the model, and post-process the results:
+
+```py
+>>> import torch
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+
+>>> logits = outputs.logits_per_image[0]
+>>> probs = logits.softmax(dim=-1).numpy()
+>>> scores = probs.tolist()
+
+>>> result = [
+...     {"score": score, "label": candidate_label}
+...     for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
+... ]
+
+>>> result
+[{'score': 0.998572, 'label': 'car'},
+ {'score': 0.0010570387, 'label': 'bike'},
+ {'score': 0.0003393686, 'label': 'tree'},
+ {'score': 3.1572064e-05, 'label': 'cat'}]
+```
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