docs: add celeba example (#143)

* docs: add celeba example

* docs: add put together and label

* docs: add put together and label

Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>

* docs: add put together and label

Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>

* docs: complete prepare data

* docs: minor updates on code

Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>

docs/get-started/celeba.md

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+# Finetuning Pre-Trained ResNet on CelebA Dataset
+
+In this example, we want to "tune" the pre-trained [ResNet](https://arxiv.org/abs/1512.03385) on [CelebA dataset](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html), the ResNet model has pre-trained weights on ImageNet.
+
+Precisely, "tuning" means: 
+- we set up a Jina search pipeline and will look at the top-K visually similar result;
+- we accept or reject the results based on their quality;
+- we let the model to remember our feedback and produce better search result.
+
+Hopefully the procedure converges after several rounds; and we get a tuned embedding for better celebrity face search.
+
+## Build embedding model
+
+Let's import pre-trained ResNet as our {ref}`embedding model<embedding-model>` using any of the following frameworks.
+
+````{tab} PyTorch
+
+```python
+import torchvision
+
+model = torchvision.models.resnet50(pretrained=True)
+```
+
+````
+````{tab} Keras
+```python
+import tensorflow as tf
+
+model = tf.keras.applications.resnet50.ResNet50(weights='imagenet')
+```
+````
+````{tab} Paddle
+```python
+import paddle
+
+model = paddle.vision.models.resnet50(pretrained=True)
+```
+````
+
+## Prepare data
+
+Now prepare CelebA data for the Finetuner. Note that Finetuner accepts Jina `DocumentArray`/`DocumentArrayMemmap`, so we first convert them into this format.
+
+Let's first make sure you have downloaded all the images `img_align_celeba.zip` (unzip) and `IdentityCelebA.txt` locally.
+
+Since each celebrity has multiple facial images, we first create a `defaultdict` and group these images by their identity:
+
+```python
+from collections import defaultdict
+
+DATA_PATH = '~/[YOUR-DIRECTORY]/img_align_celeba/'
+IDENTITY_PATH = '~/[YOUR-DIRECTORY]/identity_CelebA.txt'
+
+
+def group_imgs_by_identity():
+    grouped = defaultdict(list)
+    with open(IDENTITY_PATH, 'r') as f:
+        for line in f:
+            img_file_name, identity = line.split()
+            grouped[identity].append(img_file_name)
+    return grouped
+```
+
+Then we create a data generator that yields every image as a `Document` object:
+
+```python
+from jina import Document
+
+def train_generator():
+    for identity, imgs in group_imgs_by_identity().items():
+        for img in imgs:
+            d = Document(uri=DATA_PATH + img)
+            d.convert_image_uri_to_blob(color_axis=0)
+            d.convert_uri_to_datauri()
+            yield d
+```
+
+
+## Put together
+
+Finally, let's feed the model and the data into the Finetuner:
+
+```python
+rv = fit(
+    model=model,
+    interactive=True,
+    train_data=train_generator,
+    freeze=True,
+    input_size=(3, 224, 224),
+    output_dim=512,  # Chop-off the last fc layer and add a trainable linear layer.
+)
+```
+
+## Label interactively
+
+You can now label the data by mouse/keyboard. The model will get trained and improved as you are labeling.
+
+From the backend you will see model's training procedure:
+
+```bash
+           Flow@22900[I]:🎉 Flow is ready to use!
+	🔗 Protocol: 		HTTP
+	🏠 Local access:	0.0.0.0:52621
+	🔒 Private network:	172.18.1.109:52621
+	🌐 Public address:	94.135.231.132:52621
+	💬 Swagger UI:		http://localhost:52621/docs
+	📚 Redoc:		http://localhost:52621/redoc
+           JINA@22900[I]:Finetuner is available at http://localhost:52621/finetuner
+```