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Feb 10, 2025
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update paligemma 2 #552

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update paligemma 2
bebechien Feb 10, 2025
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bebechien Feb 10, 2025
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10 changes: 5 additions & 5 deletions site/en/gemma/docs/paligemma/inference-with-keras.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -119,7 +119,7 @@
"Before using PaliGemma for the first time, you must request access to the model through Kaggle by completing the following steps:\n",
"\n",
"1. Log in to [Kaggle](https://www.kaggle.com), or create a new Kaggle account if you don't already have one.\n",
"1. Go to the [PaliGemma model card](https://www.kaggle.com/models/google/paligemma/) and click **Request Access**.\n",
"1. Go to the [PaliGemma model card](https://www.kaggle.com/models/google/paligemma-2/) and click **Request Access**.\n",
"1. Complete the consent form and accept the terms and conditions."
]
},
Expand Down Expand Up @@ -200,7 +200,7 @@
},
"outputs": [],
"source": [
"!pip install -U -q keras-nlp"
"!pip install -U -q keras-nlp kagglehub"
]
},
{
Expand Down Expand Up @@ -260,7 +260,7 @@
},
"outputs": [],
"source": [
"paligemma = keras_hub.models.PaliGemmaCausalLM.from_preset(\"pali_gemma_3b_mix_224\")\n",
"paligemma = keras_hub.models.PaliGemmaCausalLM.from_preset(\"kaggle://keras/paligemma2/keras/pali_gemma_2_mix_3b_224\")\n",
"paligemma.summary()"
]
},
Expand Down Expand Up @@ -302,7 +302,7 @@
" image = PIL.Image.open(contents)\n",
" image = crop_and_resize(image, target_size)\n",
" image = np.array(image)\n",
" # Remove alpha channel if neccessary.\n",
" # Remove alpha channel if necessary.\n",
" if image.shape[2] == 4:\n",
" image = image[:, :, :3]\n",
" return image\n",
Expand Down Expand Up @@ -492,7 +492,7 @@
"source": [
"### Use `segment` prompt\n",
"\n",
"The following example code uses the `segment` prompt syntax to locate the area of an image occupied by an object. It uses the Google `big_vision` library to interpret the model output and generate a mask for the segemented object.\n",
"The following example code uses the `segment` prompt syntax to locate the area of an image occupied by an object. It uses the Google `big_vision` library to interpret the model output and generate a mask for the segmented object.\n",
"\n",
"Before getting started, install the `big_vision` library and its dependencies, as shown in this code example:\n"
]
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