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Merge pull request #110 from bebechien/fix
Fix colab link and nbfmt
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PaliGemma/Finetune_PaliGemma_for_object_detection.ipynb

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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License. "
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"# limitations under the License."
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{
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/drive/13fSUAhRviSI1YKhOmesjktw5BaI87sfm?usp=sharing\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/PaliGemma/Finetune_PaliGemma_for_object_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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"## Conclusion\n",
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"\n",
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"This notebook demonstrated the fine-tuning of Paligemma on a fashion dataset for object detection. While the complete dataset of 4.5k images could not be utilized due to resource constraints, a subset of 640 images was used, which resulted in measurable improvements in detecting clothing items. \n",
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"This notebook demonstrated the fine-tuning of Paligemma on a fashion dataset for object detection. While the complete dataset of 4.5k images could not be utilized due to resource constraints, a subset of 640 images was used, which resulted in measurable improvements in detecting clothing items.\n",
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"\n",
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"Model was fine-tuned for 80 steps on T4 GPUs;\n",
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"Loss was reduced from 4.7220 to 2.5054.\n",

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