parser failed on RLE mask encoding with size mismatch error

Not specified by the original author.

TL;DR

Fix this in the annotation platform by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

parser failed on RLE mask encoding with size mismatch error

Use this when

• The operation in the annotation platform fails with this exact message: parser failed on RLE mask encoding with size mismatch error.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the annotation platform, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Verify: parse the full file and confirm row counts match the source export.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Compare the result counts, decisions or alerts against the source in the annotation platform so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in the annotation platform on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Parsers fail on the first malformed record; a truncated array, extra field or bad byte stops the whole import unless the reader isolates bad rows.

Variant phrasings

parser failed on RLE mask encoding with size mismatch error

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: parser failed on RLE mask encoding with size mismatch error

The how-to phrasing for the same failure in the annotation platform. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

the annotation platform troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: the annotation platform at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_cIVKxLaHkwMUIg3PqYwuew

Resolved from the public thread: https://vectle.com/posts/pst_cIVKxLaHkwMUIg3PqYwuew

Thread query: parser failed on RLE mask encoding with size mismatch error

TL;DR: Fix this in the annotation platform by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in Labelbox by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

mask decoding failed

Use this when

• The operation in Labelbox fails with this exact message: mask decoding failed.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Labelbox, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Verify: parse the full file and confirm row counts match the source export.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Labelbox on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in Labelbox so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Parsers fail on the first malformed record; a truncated array, extra field or bad byte stops the whole import unless the reader isolates bad rows.

Variant phrasings

mask decoding failed

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: Labelbox COCO export failed error "mask decoding failed"

The how-to phrasing for the same failure in Labelbox. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Labelbox troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Labelbox at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_GabXiW0y6MqH27WBQLbiQQ

Resolved from the public thread: https://vectle.com/posts/pst_GabXiW0y6MqH27WBQLbiQQ

Thread query: Labelbox COCO export failed error "mask decoding failed"

TL;DR: Fix this in Labelbox by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in COCO by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

RLE segmentation size mismatch in COCO export

Use this when

• The operation in COCO fails with this exact message: RLE segmentation size mismatch in COCO export.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in COCO, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Verify: confirm the single item now succeeds before rerunning the full batch.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Compare the result counts, decisions or alerts against the source in COCO so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in COCO on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

The failure is deterministic for the inputs involved, so a full batch retry repeats it; isolating the smallest failing item exposes the exact input or setting responsible.

Variant phrasings

RLE segmentation size mismatch in COCO export

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: how to fix annotation failure "RLE segmentation size mismatch in COCO

The how-to phrasing for the same failure in COCO. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

COCO troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: COCO at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Ccsyi4T47I9Ib9RdmXHNlw

Resolved from the public thread: https://vectle.com/posts/pst_Ccsyi4T47I9Ib9RdmXHNlw

Thread query: how to fix annotation failure "RLE segmentation size mismatch in COCO export"

TL;DR: Fix this in COCO by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.