This is impletation of Retrieval Augmented Generation (RAG)with Pincone, Llmam2 and Langchain
Get your Pinecone api key, environment and index name then modify your key, enviroment and index name in line 20, 21, 30 of run.py
pip install -r requirements.txt
python run.py
YOLOv7 outperform which models
[Document(page_content='From the results we see that if compared with YOLOv4,\nYOLOv7 has 75% less parameters, 36% less computation,\nand brings 1.5% higher AP. If compared with state-of-the-\nart YOLOR-CSP, YOLOv7 has 43% fewer parameters, 15%\nless computation, and 0.4% higher AP. In the performance\nof tiny model, compared with YOLOv4-tiny-31, YOLOv7-\ntiny reduces the number of parameters by 39% and the\namount of computation by 49%, but maintains the same AP.\nOn the cloud GPU model, our model can still have a higher'), Document(page_content='From the results we see that if compared with YOLOv4,\nYOLOv7 has 75% less parameters, 36% less computation,\nand brings 1.5% higher AP. If compared with state-of-the-\nart YOLOR-CSP, YOLOv7 has 43% fewer parameters, 15%\nless computation, and 0.4% higher AP. In the performance\nof tiny model, compared with YOLOv4-tiny-31, YOLOv7-\ntiny reduces the number of parameters by 39% and the\namount of computation by 49%, but maintains the same AP.\nOn the cloud GPU model, our model can still have a higher'), Document(page_content='From the results we see that if compared with YOLOv4,\nYOLOv7 has 75% less parameters, 36% less computation,\nand brings 1.5% higher AP. If compared with state-of-the-\nart YOLOR-CSP, YOLOv7 has 43% fewer parameters, 15%\nless computation, and 0.4% higher AP. In the performance\nof tiny model, compared with YOLOv4-tiny-31, YOLOv7-\ntiny reduces the number of parameters by 39% and the\namount of computation by 49%, but maintains the same AP.\nOn the cloud GPU model, our model can still have a higher'), Document(page_content='From the results we see that if compared with YOLOv4,\nYOLOv7 has 75% less parameters, 36% less computation,\nand brings 1.5% higher AP. If compared with state-of-the-\nart YOLOR-CSP, YOLOv7 has 43% fewer parameters, 15%\nless computation, and 0.4% higher AP. In the performance\nof tiny model, compared with YOLOv4-tiny-31, YOLOv7-\ntiny reduces the number of parameters by 39% and the\namount of computation by 49%, but maintains the same AP.\nOn the cloud GPU model, our model can still have a higher')]
Based on the results provided, YOLOv7 outperforms both YOLOv4 and YOLOR-CSP on the given tasks.{'output_text': ' Based on the results provided, YOLOv7 outperforms both YOLOv4 and YOLOR-CSP on the given tasks.'}