Various Qwen VLM runs:
1. CPU only:
{
"run_id": "run-62ce7f48918e",
"agent_id": "qwen-vlm-estimator",
"start_time": null,
"end_time": null,
"model_version": "Qwen/Qwen2.5-VL-7B-Instruct",
"gps": {
"raw_tags": {}
},
"question": "Estimate area of unoccupied spots in the parking lot in square meters",
"vlm_raw_response": {
"answer_text": "```json\n{\n \"answer_text\": \"The estimated area of unoccupied spots in the parking lot is approximately 300 square meters.\",\n \"parking_spot_count\": 16,\n \"assumptions\": [\n \"Each parking spot is assumed to be 4.5m x 1.8m.\",\n \"There is a 1.2 spacing factor between each spot.\"\n ],\n \"computed\": {\n \"total_area_meters\": 300,\n \"total_area_feet\": 3229.17\n }\n}\n```",
"raw": "```json\n{\n \"answer_text\": \"The estimated area of unoccupied spots in the parking lot is approximately 300 square meters.\",\n \"parking_spot_count\": 16,\n \"assumptions\": [\n \"Each parking spot is assumed to be 4.5m x 1.8m.\",\n \"There is a 1.2 spacing factor between each spot.\"\n ],\n \"computed\": {\n \"total_area_meters\": 300,\n \"total_area_feet\": 3229.17\n }\n}\n```"
},
"parking_spot_count_used": 300,
"assumptions": [
"sedan footprint 4.5m x 1.8m",
"spacing factor 1.2"
],
"computed": {
"area_m2": 2916.0,
"area_ft2": 31387.53,
"spot_area_m2": 9.72
},
"answer_text": "Estimated total area \u2248 2916.0 m\u00b2 (31387.53 ft\u00b2) based on 300 parking spots and assumed sedan footprint 4.5m x 1.8m with spacing factor 1.2."
}
2. GPU:0
{
"run_id": "run-3f3096d053f1",
"agent_id": "qwen-vlm-estimator",
"start_time": null,
"end_time": null,
"model_version": "Qwen/Qwen2.5-VL-3B-Instruct",
"gps": {
"raw_tags": {}
},
"question": "Estimate the number of unoccupied parking spots. Return JSON only.",
"vlm_raw_response": {
"answer_text": "The image shows an aerial view of a parking lot with several cars parked in it.",
"parking_spot_count": 10,
"assumptions": [
"A typical U.S. sedan footprint is approximately 4.5 meters by 1.8 meters.",
"There is a spacing factor of 1.2 to account for drive lanes."
],
"computed": {
"total_area_square_meters": 360,
"total_area_square_feet": 3903.72
}
},
"parking_spot_count_used": 10,
"assumptions": [
"sedan footprint 4.5m x 1.8m",
"spacing factor 1.2"
],
"computed": {
"area_m2": 97.2,
"area_ft2": 1046.25,
"spot_area_m2": 9.72
},
"answer_text": "Estimated total area \u2248 97.2 m\u00b2 (1046.25 ft\u00b2) based on 10 parking spots and assumed sedan footprint 4.5m x 1.8m with spacing factor 1.2."
}
3. GPU-7B:
{
"run_id": "run-bd2ed2e3ab83",
"agent_id": "qwen-vlm-estimator",
"start_time": null,
"end_time": null,
"model_version": "Qwen/Qwen2.5-VL-7B-Instruct",
"gps": {
"raw_tags": {}
},
"question": "Estimate the number of occupied parking spots. Return JSON only.",
"vlm_raw_response": {
"error": "CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 7.96 GiB of which 0 bytes is free. Of the allocated memory 14.36 GiB is allocated by PyTorch, and 84.71 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)"
},
"parking_spot_count_used": 10,
"assumptions": [
"sedan footprint 4.5m x 1.8m",
"spacing factor 1.2"
],
"computed": {
"area_m2": 97.2,
"area_ft2": 1046.25,
"spot_area_m2": 9.72
},
"answer_text": "Estimated total area \u2248 97.2 m\u00b2 (1046.25 ft\u00b2) based on 10 parking spots and assumed sedan footprint 4.5m x 1.8m with spacing factor 1.2."
}
Reference: https://1drv.ms/w/c/d609fb70e39b65c8/IQC8kLmnNZGJTaPHbAQfT7nkAbjuAmYv60BKUrrTotz-ou4?e=jnFy7U
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