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.'}