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Merged
JoanFM merged 6 commits into from
Sep 30, 2022
Merged

docs: adjust readme according to suggestions #577

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a959710
docs: adjust readme according to suggestions
Sep 28, 2022
7528f4b
fix: apply suggestions from code review
Sep 29, 2022
102c0cb
Merge branch 'main' into fix-example-4-readme
Sep 29, 2022
fa9594a
docs: set amount of RAM needed for example
Sep 29, 2022
628576f
Merge branch 'fix-example-4-readme' of https://github.com/jina-ai/doc…
Sep 29, 2022
e6cb3b0
fix: update README.md
Sep 29, 2022
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17 changes: 10 additions & 7 deletions README.md
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Expand Up @@ -195,9 +195,13 @@ Or you can simply pull it from Jina Cloud:
left_da = DocumentArray.pull('demo-leftda', show_progress=True)
```

**Note**
If you have more than 15GB of RAM and want to try using the whole dataset instead of just the first 1000 images, remove [:1000] when loading the files into the DocumentArrays left_da and right_da.


You will see a running progress bar to indicate the downloading process.

To get a feeling of the data you will handle, plot them in one sprite image:
To get a feeling of the data you will handle, plot them in one sprite image. You will need to have matplotlib and torch installed to run this snippet:

```python
left_da.plot_image_sprites()
Expand Down Expand Up @@ -243,7 +247,7 @@ This step takes ~30 seconds on GPU. Beside PyTorch, you can also use TensorFlow,

### Visualize embeddings

You can visualize the embeddings via tSNE in an interactive embedding projector:
You can visualize the embeddings via tSNE in an interactive embedding projector. You will need to have pydantic, uvicorn and fastapi installed to run this snippet:

```python
left_da.plot_embeddings(image_sprites=True)
Expand All @@ -268,7 +272,7 @@ Fun is fun, but recall our goal is to match left images against right images and
right_da = (
DocumentArray.pull('demo-rightda', show_progress=True)
.apply(preproc)
.embed(model, device='cuda')
.embed(model, device='cuda')[:1000]
)
```

Expand All @@ -277,7 +281,7 @@ right_da = (

```python
right_da = (
DocumentArray.from_files('right/*.jpg').apply(preproc).embed(model, device='cuda')
DocumentArray.from_files('right/*.jpg')[:1000].apply(preproc).embed(model, device='cuda')
)
```

Expand All @@ -296,9 +300,8 @@ left_da.match(right_da, limit=9)
Let's inspect what's inside `left_da` matches now:

```python
for d in left_da:
for m in d.matches:
print(d.uri, m.uri, m.scores['cosine'].value)
for m in left_da[0].matches:
print(d.uri, m.uri, m.scores['cosine'].value)
```

```text
Expand Down