diff --git a/documentation/llm/sequence_level_with_taxonomy.txt b/documentation/llm/sequence_level_with_taxonomy.txt new file mode 100644 index 00000000..508e36fb --- /dev/null +++ b/documentation/llm/sequence_level_with_taxonomy.txt @@ -0,0 +1,38243 @@ +{ + "conversation_id": "0bb08249-89ae-4034-8899-ed92a7e7f4b0", + "next_message": null, + "history": [ + { + "user": { + "additional_context": "", + "env_context": { + "env_state": { + "operating_system": "linux", + "current_working_directory": "/home/jesse/tnc-projects/animl/animl-api", + "environment_variables": [] + } + }, + "content": { + "Prompt": { + "prompt": "Hello Q. I will be trying to adapt the existing src/scripts/analyzeMLSequenceLevel.js to work with SpeciesNet in a similar way I adapted analyzeMLObjectLevel.js to analyzeMLObjectLevelWithTaxonomy.js. To do this I need to first understand what's going on in src/scripts/analyzeMLSequenceLevel.js and then refactor it. To start, walk me through src/scripts/analyzeMLSequenceLevel.js chunk by chunk so that I can understand what the script is doing" + } + }, + "timestamp": "2025-11-21T11:42:00.596539842-05:00", + "images": null + }, + "assistant": { + "ToolUse": { + "message_id": "75ac643e-3c2d-4ccd-a0ac-901c184473d0", + "content": "I'll read the `analyzeMLSequenceLevel.js` script and walk you through it chunk by chunk to help you understand what it's doing before you adapt it for SpeciesNet.", + "tool_uses": [ + { + "id": "tooluse_gztRSlOuRYO9eb3_9Gw7UQ", + "name": "fs_read", + "orig_name": "fs_read", + "args": { + "operations": [ + { + "mode": "Line", + "path": "/home/jesse/tnc-projects/animl/animl-api/src/scripts/analyzeMLSequenceLevel.js" + } + ] + }, + "orig_args": { + "operations": [ + { + "mode": "Line", + "path": "/home/jesse/tnc-projects/animl/animl-api/src/scripts/analyzeMLSequenceLevel.js" + } + ] + } + } + ] + } + }, + "request_metadata": { + "request_id": 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"tool_use_results": [ + { + "tool_use_id": "tooluse_gztRSlOuRYO9eb3_9Gw7UQ", + "content": [ + { + "Text": "import fs from 'node:fs';\nimport path from 'node:path';\nimport stream from 'node:stream/promises';\nimport { DateTime } from 'luxon';\nimport appRoot from 'app-root-path';\nimport { connectToDatabase } from '../../.build/api/db/connect.js';\nimport { getConfig } from '../../.build/config/config.js';\nimport { analysisConfig, reportColumns } from './analysisConfig.js';\nimport Image from '../../.build/api/db/schemas/Image.js';\nimport { ProjectModel } from '../../.build/api/db/models/Project.js';\nimport { stringify } from 'csv-stringify';\nimport cliProgress from 'cli-progress';\n\n/*\n * Script to analyze ML model performance at the sequence (bursts of images) level\n *\n * NOTE: you can use this script to analyze the performance of MegaDetector independently,\n * or of a classifier that's been paired with an object detector in an inference pipeline.\n * Keep in mind that if assessing the latter, a true positive would mean that\n * (a) the object detector correctly identified the object, and (b) the classifier correctly identified the class.\n * So a false negative _could_ mean that the object detector correctly identified the object,\n * but the classifier incorrectly identified the class.\n *\n * The reason that's worth noting is because at the moment it doesn't support evaluating the performance\n * of a classifier independently of an object detector.\n *\n * ALSO NOTE: it is assumed that the model being analyzed was used for the entire duration of the date range.\n * This script, and Animl in general, doesn't know when a model was deployed, renamed, or automation rules applied,\n * and we currently do not store inference _request_ data at the image level (though we should)\n * so it's up to the user to ensure the model was used for the entire date range.\n *\n * If Animl never requested inference for the model being analyzed for some image(s) in the date range,\n * but there are validating labels in those images, those images will be counted as false negatives,\n * which will significantly skew the results (model will appear to to have worse recall than it actually does).\n *\n * command to run script:\n * STAGE=prod AWS_PROFILE=animl REGION=us-west-2 node ./src/scripts/analyzeMLSequenceLevel.js\n */\n\nconst { ANALYSIS_DIR, PROJECT_ID, START_DATE, END_DATE, ML_MODEL, MAX_SEQUENCE_DELTA } =\n analysisConfig;\n\nconst TARGET_CLASSES = analysisConfig.TARGET_CLASSES.map((tc) => ({\n predicted_id: tc.predicted.split(':')[1],\n validation_ids: tc.validation.map((v) => v.split(':')[1]),\n predicted_name: tc.predicted.split(':')[0],\n validation_names: tc.validation.map((v) => v.split(':')[0]),\n}));\n\nasync function writeConfigToFile(filename, analysisPath, config) {\n const jsonFilename = path.join(analysisPath, `${filename}_config.json`);\n\n if (!fs.existsSync(analysisPath)) {\n fs.mkdirSync(analysisPath, { recursive: true });\n }\n\n try {\n const data = JSON.stringify(config, null, 2);\n await fs.writeFileSync(jsonFilename, data, 'utf8');\n } catch (err) {\n throw console.log(err instanceof Error ? err.message : String(err));\n }\n}\n\nconst buildBasePipeline = (projectId, startDate, endDate) => [\n // return reviewed images for a camera between two dates\n {\n $match: {\n projectId: projectId,\n dateAdded: {\n $gte: new Date(startDate),\n $lt: new Date(endDate),\n },\n reviewed: true,\n },\n },\n\n // set the firstValidLabel field\n {\n $set: {\n objects: {\n $map: {\n input: '$objects',\n as: 'obj',\n in: {\n $setField: {\n field: 'firstValidLabel',\n input: '$$obj',\n value: {\n $filter: {\n input: '$$obj.labels',\n as: 'label',\n cond: {\n $eq: ['$$label.validation.validated', true],\n },\n limit: 1,\n },\n },\n },\n },\n },\n },\n },\n },\n];\n\n// Does the firstValidLabel validate the prediction\n// e.g., for a \"rodent\" prediction, a firstValidLabel of [\"rodent\", \"mouse, \"rat\"]\n// would validate the prediction as being a true positive\nfunction FVLValidatesPrediction(obj, tClass) {\n // if no firstValidLabel, all labels have been invalidated, so return false\n if (obj.firstValidLabel.length === 0) return false;\n const fvl = obj.firstValidLabel[0]?.labelId;\n // if the ml model is megadetector and the target class is '1' (animal),\n // any firstValidLabel that is not is a 'person' or 'vehicle' or 'empty'\n // would validate the prediction\n if (ML_MODEL.includes('megadetector') && tClass.predicted_id === '1') {\n return fvl !== '2' && fvl !== '3' && fvl !== 'empty';\n } else {\n return tClass.validation_ids.includes(fvl);\n }\n}\n\n// ACTUAL - object must be:\n// (a) locked, (b) has a first valid label that validates the prediction/target class,\n// (i.e., for \"rodent\" prediction, a firstValidLabel of [\"rodent\", \"mouse, \"rat\"]),\nconst isActual = (obj, tClass) => obj.locked && FVLValidatesPrediction(obj, tClass);\n\n// TRUE POSITIVE - object must be:\n// (a) locked, (b) has an ml-predicted label of the target class, and\n// (c) has a first valid label that validates the prediction/target class,\n// (i.e., for \"rodent\" prediction, a firstValidLabel of [\"rodent\", \"mouse, \"rat\"]),\nconst isTruePositive = (obj, tClass) =>\n obj.locked &&\n obj.labels.some(\n (l) => l.type === 'ml' && l.mlModel === ML_MODEL && l.labelId === tClass.predicted_id,\n ) &&\n FVLValidatesPrediction(obj, tClass);\n\n// FALSE POSITIVE - object must be:\n// (a) locked, (b) has an ml-predicted label of the target class, and\n// (c) DOES NOT have a first valid label that validates the prediction/target class\nconst isFalsePositive = (obj, tClass) =>\n obj.locked &&\n obj.labels.some(\n (l) => l.type === 'ml' && l.mlModel === ML_MODEL && l.labelId === tClass.predicted_id,\n ) &&\n !FVLValidatesPrediction(obj, tClass);\n\n// // FALSE NEGATIVE - object must be:\n// // (a) locked, (b) does NOT have an ml-predicted label of the target class, and\n// // (c) has a first valid label that validates the prediction/target class,\n// // (i.e., for \"rodent\" prediction, a firstValidLabel of [\"rodent\", \"mouse, \"rat\"]),\n// const isFalseNegative = (obj, tClass) =>\n// obj.locked &&\n// !obj.labels.some(\n// (l) => l.type === 'ml' && l.mlModel === ML_MODEL && l.labelId === tClass.predicted_id,\n// ) &&\n// FVLValidatesPrediction(obj, tClass);\n\nasync function getCount(pipeline) {\n console.log('getting image count');\n let count = null;\n try {\n const pipelineCopy = structuredClone(pipeline);\n pipelineCopy.push({ $count: 'count' });\n const res = await Image.aggregate(pipelineCopy);\n count = res[0] ? res[0].count : 0;\n } catch (err) {\n console.log('error counting Image: ', err);\n }\n return count;\n}\n\nfunction processSequence(sequence, deployment, data) {\n for (const tClass of TARGET_CLASSES) {\n const key = `${deployment._id}_${tClass.predicted_id}`;\n let hasActual = false;\n let hasTruePositive = false;\n let hasFalsePositive = false;\n\n for (const img of sequence) {\n for (const obj of img.objects) {\n if (!hasActual) {\n hasActual = isActual(obj, tClass);\n }\n\n if (!hasTruePositive) {\n hasTruePositive = isTruePositive(obj, tClass);\n }\n\n if (!hasFalsePositive) {\n hasFalsePositive = isFalsePositive(obj, tClass);\n }\n }\n }\n\n if (hasActual) data[key].allActuals++;\n if (hasTruePositive) data[key].truePositives++;\n if (hasActual && !hasTruePositive) data[key].falseNegatives++;\n if (!hasActual && hasFalsePositive) data[key].falsePositives++;\n }\n return data;\n}\n\n// main function\nasync function analyze() {\n console.log(\n `Analyzing ${ML_MODEL} performance in ${PROJECT_ID} Project between ${START_DATE} and ${END_DATE} at the sequence level...`,\n );\n console.log('Getting config...');\n const config = await getConfig();\n console.log('Connecting to db...');\n const dbClient = await connectToDatabase(config);\n\n try {\n // set up data structure to hold results\n const project = await ProjectModel.queryById(PROJECT_ID);\n const cameraConfigs = project.cameraConfigs;\n let data = {};\n const deployments = [];\n cameraConfigs.forEach((cc) => {\n for (const dep of cc.deployments) {\n if (dep.name === 'default') continue; // skip default deployments\n deployments.push(dep);\n for (const tClass of TARGET_CLASSES) {\n data[`${dep._id}_${tClass.predicted_id}`] = {\n cameraId: cc._id,\n deploymentName: dep.name,\n targetClass: tClass.predicted_name,\n validationClasses: tClass.validation_names.join(', '),\n allActuals: 0,\n truePositives: 0,\n falsePositives: 0,\n falseNegatives: 0,\n precision: null,\n recall: null,\n f1: null,\n };\n }\n }\n });\n\n // init reports\n const dt = DateTime.now().setZone('utc').toFormat(\"yyyy-LL-dd'T'HHmm'Z'\");\n const analysisPath = path.join(appRoot.path, ANALYSIS_DIR);\n if (!fs.existsSync(analysisPath)) {\n fs.mkdirSync(analysisPath, { recursive: true });\n }\n\n const root = `${PROJECT_ID}_${ML_MODEL}_${START_DATE}--${END_DATE}_sequence-level_${dt}`;\n await writeConfigToFile(root, analysisPath, analysisConfig);\n\n const csvFilename = path.join(analysisPath, `${root}.csv`);\n const writableStream = fs.createWriteStream(csvFilename);\n const stringifier = stringify({ header: true, columns: reportColumns });\n stringifier.on('error', (err) => console.error(err.message));\n\n // get image count\n const aggPipeline = buildBasePipeline(PROJECT_ID, START_DATE, END_DATE);\n const imgCount = await getCount(aggPipeline);\n console.log('image count: ', imgCount);\n const progress = new cliProgress.SingleBar({}, cliProgress.Presets.shades_classic);\n progress.start(imgCount, 0);\n\n // for each deployment, stream in images in chronological order\n // group them into sequences,\n // and process each sequence to count TPs, FPs, and FNs\n for (const dep of deployments) {\n // if (dep.name !== 'Willows beach') continue;\n // create aggregation pipeline\n const depPipeline = structuredClone(aggPipeline);\n depPipeline[0].$match.deploymentId = dep._id;\n depPipeline.push({ $sort: { dateTimeOriginal: 1 } });\n\n let sequence = [];\n const depImageCount = await Image.aggregate(depPipeline);\n let processedCount = 0;\n\n for await (const img of Image.aggregate(depPipeline)) {\n if (sequence.length === 0) {\n sequence.push(img);\n continue;\n }\n\n const lastImg = sequence[sequence.length - 1];\n const imgDateAdded = DateTime.fromJSDate(img.dateTimeOriginal);\n const lastImgDateAdded = DateTime.fromJSDate(lastImg.dateTimeOriginal);\n const diff = lastImgDateAdded.diff(imgDateAdded, 'seconds').toObject();\n const delta = Math.abs(diff.seconds);\n\n // if the delta between the last image and the current image is less than the max sequence delta,\n if (delta <= MAX_SEQUENCE_DELTA) {\n // image belongs to current sequence\n sequence.push(img);\n } else {\n // found a gap,\n // process images previously assigned to sequence and reset sequence\n data = processSequence(sequence, dep, data);\n sequence = [img];\n }\n processedCount++;\n\n // we've reached the end of the deployment\n if (processedCount === depImageCount.length - 1) {\n data = processSequence(sequence, dep, data);\n }\n\n progress.increment();\n }\n }\n\n progress.stop();\n console.log(`\\nAnalysis complete. Writing results to ${csvFilename}`);\n\n // write results to csv\n for (const value of Object.values(data)) {\n // calculate precision, recall, and F1 score\n const TP = value.truePositives;\n const FP = value.falsePositives;\n const FN = value.falseNegatives;\n const precision = TP / (TP + FP);\n const recall = TP / (TP + FN);\n const f1 = (2 * precision * recall) / (precision + recall); // harmonic mean\n\n stringifier.write({\n ...value,\n allActuals: value.allActuals,\n truePositives: TP,\n falsePositives: FP,\n falseNegatives: FN,\n precision: Number.parseFloat(precision * 100).toFixed(2),\n recall: Number.parseFloat(recall * 100).toFixed(2),\n f1: Number.parseFloat(f1).toFixed(2),\n });\n }\n\n // add rows for target class totals\n for (const tClass of TARGET_CLASSES) {\n const tClassRows = Object.values(data).filter((v) => v.targetClass === tClass.predicted_name);\n\n const totalActuals = tClassRows.reduce((acc, v) => acc + v.allActuals, 0);\n const totalTP = tClassRows.reduce((acc, v) => acc + v.truePositives, 0);\n const totalFP = tClassRows.reduce((acc, v) => acc + v.falsePositives, 0);\n const totalFN = tClassRows.reduce((acc, v) => acc + v.falseNegatives, 0);\n const precision = totalTP / (totalTP + totalFP);\n const recall = totalTP / (totalTP + totalFN);\n const f1 = (2 * precision * recall) / (precision + recall);\n\n stringifier.write({\n cameraId: 'total',\n deploymentName: 'total',\n targetClass: tClass.predicted_name,\n validationClasses: tClass.validation_names.join(', '),\n allActuals: totalActuals,\n truePositives: totalTP,\n falsePositives: totalFP,\n falseNegatives: totalFN,\n precision: Number.parseFloat(precision * 100).toFixed(2),\n recall: Number.parseFloat(recall * 100).toFixed(2),\n f1: Number.parseFloat(f1).toFixed(2),\n });\n }\n\n stringifier.end();\n\n await stream.pipeline(stringifier, writableStream);\n\n dbClient.connection.close();\n process.exit(0);\n } catch (err) {\n dbClient.connection.close();\n console.log(err);\n }\n}\n\nanalyze();" + } + ], + "status": "Success" + } + ] + } + }, + "timestamp": null, + "images": null + }, + "assistant": { + "Response": { + "message_id": "4feda3c2-5bff-4928-b38b-90996998f244", + "content": "Now let me walk you through this script chunk by chunk:\n\n## 1. Imports and Setup (lines 1-11)\n```javascript\nimport fs from 'node:fs';\nimport path from 'node:path';\n// ... other imports\n```\nStandard imports for file system operations, database connections, CSV generation, and progress bars.\n\n## 2. Configuration and Comments (lines 13-42)\nThe script analyzes ML model performance at the **sequence level** (bursts of images taken close together in time). Key points:\n- Can analyze MegaDetector alone or classifier+detector pipelines\n- Assumes the model was used for the entire date range\n- Missing inference requests will skew results as false negatives\n\n## 3. Configuration Processing (lines 44-52)\n```javascript\nconst TARGET_CLASSES = analysisConfig.TARGET_CLASSES.map((tc) => ({\n predicted_id: tc.predicted.split(':')[1],\n validation_ids: tc.validation.map((v) => v.split(':')[1]),\n // ...\n}));\n```\nTransforms target classes from `\"name:id\"` format into separate name and ID fields.\n\n## 4. Base Pipeline Builder (lines 66-102)\n```javascript\nconst buildBasePipeline = (projectId, startDate, endDate) => [\n // Match reviewed images in date range\n { $match: { projectId, dateAdded: {...}, reviewed: true } },\n // Add firstValidLabel field to each object\n { $set: { objects: { $map: {...} } } }\n];\n```\nCreates MongoDB aggregation pipeline to get reviewed images and add a `firstValidLabel` field (the first validated label for each object).\n\n## 5. Validation Logic (lines 104-115)\n```javascript\nfunction FVLValidatesPrediction(obj, tClass) {\n if (obj.firstValidLabel.length === 0) return false;\n const fvl = obj.firstValidLabel[0]?.labelId;\n // Special case for MegaDetector animal class\n if (ML_MODEL.includes('megadetector') && tClass.predicted_id === '1') {\n return fvl !== '2' && fvl !== '3' && fvl !== 'empty';\n } else {\n return tClass.validation_ids.includes(fvl);\n }\n}\n```\nDetermines if the first valid label validates the ML prediction. Special handling for MegaDetector's \"animal\" class.\n\n## 6. Classification Functions (lines 117-143)\n```javascript\nconst isActual = (obj, tClass) => obj.locked && FVLValidatesPrediction(obj, tClass);\nconst isTruePositive = (obj, tClass) => /* locked + ML prediction + validates */;\nconst isFalsePositive = (obj, tClass) => /* locked + ML prediction + doesn't validate */;\n```\nDefines the core classification logic for actual positives, true positives, and false positives.\n\n## 7. Sequence Processing (lines 158-188)\n```javascript\nfunction processSequence(sequence, deployment, data) {\n for (const tClass of TARGET_CLASSES) {\n let hasActual = false;\n let hasTruePositive = false;\n let hasFalsePositive = false;\n \n // Check all images in sequence for any occurrence\n for (const img of sequence) {\n for (const obj of img.objects) {\n // Set flags if conditions are met\n }\n }\n \n // Update counters based on sequence-level flags\n if (hasActual) data[key].allActuals++;\n if (hasTruePositive) data[key].truePositives++;\n // ...\n }\n}\n```\n**Key insight**: This processes entire sequences as single units. If ANY image in a sequence has a true positive, the whole sequence counts as one true positive.\n\n## 8. Main Analysis Function (lines 191-350)\nThe main function:\n1. Sets up data structure for each deployment × target class combination\n2. Creates output files and CSV writer\n3. For each deployment:\n - Streams images in chronological order\n - Groups them into sequences based on `MAX_SEQUENCE_DELTA` time gaps\n - Processes each complete sequence\n4. Calculates precision, recall, and F1 scores\n5. Writes results to CSV with totals\n\n## 9. Sequence Grouping Logic (lines 270-295)\n```javascript\nconst delta = Math.abs(diff.seconds);\nif (delta <= MAX_SEQUENCE_DELTA) {\n sequence.push(img); // Same sequence\n} else {\n data = processSequence(sequence, dep, data); // Process and reset\n sequence = [img];\n}\n```\nImages are grouped into sequences based on time gaps. 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"content": { + "Prompt": { + "prompt": "Can you draw a diagram of the code execution flow of the script?" + } + }, + "timestamp": "2025-11-21T11:55:02.739536361-05:00", + "images": null + }, + "assistant": { + "Response": { + "message_id": "914c643e-a147-4acb-8b6f-4ee334d97a6d", + "content": "I'll create a visual diagram showing the execution flow of the `analyzeMLSequenceLevel.js` script:\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│ SCRIPT START │\n└─────────────────────────┬───────────────────────────────────────┘\n │\n┌─────────────────────────▼───────────────────────────────────────┐\n│ INITIALIZATION │\n│ • Load config & connect to DB │\n│ • Transform TARGET_CLASSES (name:id → separate fields) │\n│ • Get project & camera deployments │\n│ • Initialize data structure for each deployment×class combo │\n└─────────────────────────┬───────────────────────────────────────┘\n │\n┌─────────────────────────▼───────────────────────────────────────┐\n│ SETUP OUTPUT │\n│ • Create analysis directory │\n│ • Write config JSON file │\n│ • Setup CSV writer with headers │\n└─────────────────────────┬───────────────────────────────────────┘\n │\n┌─────────────────────────▼───────────────────────────────────────┐\n│ GET IMAGE COUNT (for progress bar) │\n│ • Run aggregation pipeline with $count │\n└─────────────────────────┬───────────────────────────────────────┘\n │\n │ ┌─────────────────────────────────────┐\n └─┤ FOR EACH DEPLOYMENT │\n └─────────────┬───────────────────────┘\n │\n ┌─────────────────────▼───────────────────────┐\n │ STREAM IMAGES (chronologically sorted) │\n │ • Build deployment-specific pipeline │\n │ • Add dateTimeOriginal sort │\n └─────────────────────┬───────────────────────┘\n │\n │ ┌───────────────────────┐\n └─┤ FOR EACH IMAGE │\n └─────┬─────────────────┘\n │\n ┌───────────────────▼───────────────────┐\n │ SEQUENCE GROUPING LOGIC │\n │ • Calculate time delta from last img │\n │ • If delta ≤ MAX_SEQUENCE_DELTA: │\n │ → Add to current sequence │\n │ • If delta > MAX_SEQUENCE_DELTA: │\n │ → Process current sequence │\n │ → Start new sequence with this img │\n └───────────────────┬───────────────────┘\n │\n ┌───────────────────▼───────────────────┐\n │ PROCESS SEQUENCE │\n │ ┌─────────────────────────────────────┤\n │ │ FOR EACH TARGET CLASS: │\n │ │ • Initialize flags (hasActual, │\n │ │ hasTruePositive, hasFalsePositive)│\n │ │ ┌───────────────────────────────────┤\n │ │ │ FOR EACH IMAGE IN SEQUENCE: │\n │ │ │ ┌─────────────────────────────────┤\n │ │ │ │ FOR EACH OBJECT IN IMAGE: │\n │ │ │ │ • Check isActual() │\n │ │ │ │ • Check isTruePositive() │\n │ │ │ │ • Check isFalsePositive() │\n │ │ │ │ • Set flags if conditions met │\n │ │ │ └─────────────────────────────────┤\n │ │ └───────────────────────────────────┤\n │ │ • Update counters based on flags: │\n │ │ - if hasActual: allActuals++ │\n │ │ - if hasTruePositive: truePos++ │\n │ │ - if hasActual && !hasTP: falseNeg++│\n │ │ - if !hasActual && hasFP: falsePos++│\n │ └─────────────────────────────────────┤\n └───────────────────┬───────────────────┘\n │\n │ Continue until all\n │ images processed\n │\n┌─────────────────────────────────────────────────▼─────────────────┐\n│ CALCULATE METRICS & WRITE RESULTS │\n│ • For each deployment×class combination: │\n│ - Calculate precision = TP/(TP+FP) │\n│ - Calculate recall = TP/(TP+FN) │\n│ - Calculate F1 = 2×precision×recall/(precision+recall) │\n│ • Write individual results to CSV │\n│ • Calculate and write totals for each target class │\n└─────────────────────────┬─────────────────────────────────────────┘\n │\n┌─────────────────────────▼─────────────────────────────────────────┐\n│ CLEANUP & EXIT │\n│ • Close CSV stream │\n│ • Close database connection │\n│ • Exit process │\n└───────────────────────────────────────────────────────────────────┘\n```\n\n**Key Flow Characteristics:**\n\n1. **Nested Loops**: Deployments → Images → Sequences → Target Classes → Objects\n2. **Sequence 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I want to start with the auxiliary functions. Stuff like setting up the output data and results structures. 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