[docs] Fix image segmentation guide (#30132)

fixes
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@ -28,8 +28,9 @@ In this guide, we will:
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install -q datasets transformers evaluate
```py
# uncomment to install the necessary libraries
!pip install -q datasets transformers evaluate accelerate
```
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
@ -236,6 +237,9 @@ Then take a look at an example:
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x683 at 0x7F9B0C201F90>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=512x683 at 0x7F9B0C201DD0>,
'scene_category': 368}
# view the image
>>> train_ds[0]["image"]
```
- `image`: a PIL image of the scene.
@ -663,15 +667,19 @@ Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. Y
</tf>
</frameworkcontent>
### Inference
Great, now that you've finetuned a model, you can use it for inference!
Load an image for inference:
Reload the dataset and load an image for inference.
```py
>>> image = ds[0]["image"]
>>> from datasets import load_dataset
>>> ds = load_dataset("scene_parse_150", split="train[:50]")
>>> ds = ds.train_test_split(test_size=0.2)
>>> test_ds = ds["test"]
>>> image = ds["test"][0]["image"]
>>> image
```
@ -749,7 +757,166 @@ Next, rescale the logits to the original image size and apply argmax on the clas
</tf>
</frameworkcontent>
To visualize the results, load the [dataset color palette](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51) as `ade_palette()` that maps each class to their RGB values. Then you can combine and plot your image and the predicted segmentation map:
To visualize the results, load the [dataset color palette](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51) as `ade_palette()` that maps each class to their RGB values.
```py
def ade_palette():
return np.asarray([
[0, 0, 0],
[120, 120, 120],
[180, 120, 120],
[6, 230, 230],
[80, 50, 50],
[4, 200, 3],
[120, 120, 80],
[140, 140, 140],
[204, 5, 255],
[230, 230, 230],
[4, 250, 7],
[224, 5, 255],
[235, 255, 7],
[150, 5, 61],
[120, 120, 70],
[8, 255, 51],
[255, 6, 82],
[143, 255, 140],
[204, 255, 4],
[255, 51, 7],
[204, 70, 3],
[0, 102, 200],
[61, 230, 250],
[255, 6, 51],
[11, 102, 255],
[255, 7, 71],
[255, 9, 224],
[9, 7, 230],
[220, 220, 220],
[255, 9, 92],
[112, 9, 255],
[8, 255, 214],
[7, 255, 224],
[255, 184, 6],
[10, 255, 71],
[255, 41, 10],
[7, 255, 255],
[224, 255, 8],
[102, 8, 255],
[255, 61, 6],
[255, 194, 7],
[255, 122, 8],
[0, 255, 20],
[255, 8, 41],
[255, 5, 153],
[6, 51, 255],
[235, 12, 255],
[160, 150, 20],
[0, 163, 255],
[140, 140, 140],
[250, 10, 15],
[20, 255, 0],
[31, 255, 0],
[255, 31, 0],
[255, 224, 0],
[153, 255, 0],
[0, 0, 255],
[255, 71, 0],
[0, 235, 255],
[0, 173, 255],
[31, 0, 255],
[11, 200, 200],
[255, 82, 0],
[0, 255, 245],
[0, 61, 255],
[0, 255, 112],
[0, 255, 133],
[255, 0, 0],
[255, 163, 0],
[255, 102, 0],
[194, 255, 0],
[0, 143, 255],
[51, 255, 0],
[0, 82, 255],
[0, 255, 41],
[0, 255, 173],
[10, 0, 255],
[173, 255, 0],
[0, 255, 153],
[255, 92, 0],
[255, 0, 255],
[255, 0, 245],
[255, 0, 102],
[255, 173, 0],
[255, 0, 20],
[255, 184, 184],
[0, 31, 255],
[0, 255, 61],
[0, 71, 255],
[255, 0, 204],
[0, 255, 194],
[0, 255, 82],
[0, 10, 255],
[0, 112, 255],
[51, 0, 255],
[0, 194, 255],
[0, 122, 255],
[0, 255, 163],
[255, 153, 0],
[0, 255, 10],
[255, 112, 0],
[143, 255, 0],
[82, 0, 255],
[163, 255, 0],
[255, 235, 0],
[8, 184, 170],
[133, 0, 255],
[0, 255, 92],
[184, 0, 255],
[255, 0, 31],
[0, 184, 255],
[0, 214, 255],
[255, 0, 112],
[92, 255, 0],
[0, 224, 255],
[112, 224, 255],
[70, 184, 160],
[163, 0, 255],
[153, 0, 255],
[71, 255, 0],
[255, 0, 163],
[255, 204, 0],
[255, 0, 143],
[0, 255, 235],
[133, 255, 0],
[255, 0, 235],
[245, 0, 255],
[255, 0, 122],
[255, 245, 0],
[10, 190, 212],
[214, 255, 0],
[0, 204, 255],
[20, 0, 255],
[255, 255, 0],
[0, 153, 255],
[0, 41, 255],
[0, 255, 204],
[41, 0, 255],
[41, 255, 0],
[173, 0, 255],
[0, 245, 255],
[71, 0, 255],
[122, 0, 255],
[0, 255, 184],
[0, 92, 255],
[184, 255, 0],
[0, 133, 255],
[255, 214, 0],
[25, 194, 194],
[102, 255, 0],
[92, 0, 255],
])
```
Then you can combine and plot your image and the predicted segmentation map:
```py
>>> import matplotlib.pyplot as plt