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fix broken image link

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ariG23498 2024-12-30 12:55:38 +05:30 committed by Ross Wightman
parent 790decc89b
commit 3a6661ac78
1 changed files with 8 additions and 8 deletions
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hfdocs/source/quickstart.mdx
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@ -164,14 +164,14 @@ First we'll need an image to do inference on. Here we load a picture of a leaf f
>>> import requests >>> import requests
>>> from PIL import Image >>> from PIL import Image
>>> from io import BytesIO >>> from io import BytesIO
>>> url = 'https://datasets-server.huggingface.co/assets/imagenet-1k/--/default/test/12/image/image.jpg' >>> url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/timm/cat.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw) >>> image = Image.open(requests.get(url, stream=True).raw)
>>> image >>> image
``` ```
Here's the image we loaded: Here's the image we loaded:
<img src="https://datasets-server.huggingface.co/assets/imagenet-1k/--/default/test/12/image/image.jpg" alt="An Image from a link" width="300"/> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/timm/cat.jpg" alt="An Image from a link" width="300"/>
Now, we'll create our model and transforms again. This time, we make sure to set our model in evaluation mode. Now, we'll create our model and transforms again. This time, we make sure to set our model in evaluation mode.
@ -211,7 +211,7 @@ Now we'll find the top 5 predicted class indexes and values using `torch.topk`.
```py ```py
>>> values, indices = torch.topk(probabilities, 5) >>> values, indices = torch.topk(probabilities, 5)
>>> indices >>> indices
tensor([162, 166, 161, 164, 167]) tensor([281, 282, 285, 673, 670])
``` ```
If we check the imagenet labels for the top index, we can see what the model predicted... If we check the imagenet labels for the top index, we can see what the model predicted...
@ -220,9 +220,9 @@ If we check the imagenet labels for the top index, we can see what the model pre
>>> IMAGENET_1k_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt' >>> IMAGENET_1k_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt'
>>> IMAGENET_1k_LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n') >>> IMAGENET_1k_LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n')
>>> [{'label': IMAGENET_1k_LABELS[idx], 'value': val.item()} for val, idx in zip(values, indices)] >>> [{'label': IMAGENET_1k_LABELS[idx], 'value': val.item()} for val, idx in zip(values, indices)]
[{'label': 'beagle', 'value': 0.8486220836639404}, [{'label': 'tabby, tabby_cat', 'value': 0.5101025700569153},
{'label': 'Walker_hound, Walker_foxhound', 'value': 0.03753996267914772}, {'label': 'tiger_cat', 'value': 0.22490699589252472},
{'label': 'basset, basset_hound', 'value': 0.024628572165966034}, {'label': 'Egyptian_cat', 'value': 0.1835290789604187},
{'label': 'bluetick', 'value': 0.010317106731235981}, {'label': 'mouse, computer_mouse', 'value': 0.006752475164830685},
{'label': 'English_foxhound', 'value': 0.006958036217838526}] {'label': 'motor_scooter, scooter', 'value': 0.004942195490002632}]
``` ```