| --- |
| license: other |
| license_name: mixed-upstream |
| license_link: LICENSE.md |
| task_categories: |
| - image-to-image |
| language: |
| - en |
| tags: |
| - image-editing |
| - instruction-based-image-editing |
| - feedback |
| - self-correction |
| - benchmark |
| pretty_name: MFRL Data |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: test |
| path: data/test-*.parquet |
| - config_name: judge |
| data_files: |
| - split: test |
| path: judge/test-*.parquet |
| dataset_info: |
| - config_name: default |
| features: |
| - name: id |
| dtype: string |
| - name: edit_type |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: source_image |
| dtype: image |
| - name: target_image |
| dtype: image |
| - name: failed_image |
| dtype: image |
| - name: feedback |
| dtype: string |
| - name: feedback_problem |
| dtype: string |
| - name: feedback_correction |
| dtype: string |
| - name: failed_image_origin |
| dtype: string |
| - name: failed_image_model |
| dtype: string |
| - name: failed_image_seed |
| dtype: int64 |
| - name: target_image_model |
| dtype: string |
| - name: target_human_confirmed |
| dtype: bool |
| - name: feedback_model |
| dtype: string |
| - name: source_dataset |
| dtype: string |
| - name: source_subset |
| dtype: string |
| - name: image_key |
| dtype: string |
| - name: split_origin |
| dtype: string |
| - name: mfrl_id |
| dtype: string |
| - name: mfrl_category |
| dtype: string |
| - config_name: judge |
| features: |
| - name: id |
| dtype: string |
| - name: edit_id |
| dtype: string |
| - name: category |
| dtype: string |
| - name: subcategory |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: source_image |
| dtype: image |
| - name: candidate_image |
| dtype: image |
| - name: label |
| dtype: string |
| - name: reason |
| dtype: string |
| - name: confidence |
| dtype: string |
| - name: generator |
| dtype: string |
| - name: label_source |
| dtype: string |
| --- |
| |
| # MFRL Data |
|
|
| Instruction-based image edits that went wrong, each paired with what should have happened and how to fix it, plus the |
| MFRL-Bench test set. |
|
|
| - **default / train: 50,642 records.** Each has a source image, an edit instruction, a ground-truth result, a failed |
| result with an obvious error, and feedback that names the problem and then gives a corrective instruction. |
| - **default / test: 3,000 records**, the edit tasks of MFRL-Bench. Each has the source, the instruction and the |
| benchmark's reference result. 2,738 of them also carry a failed image and feedback built the same way as the |
| training data. The others have none (`failed_image` and `feedback` are null), because no obvious failure was found. |
| - **judge / test: 2,000 images**, the judge track of MFRL-Bench: edited images for the test tasks, half labeled |
| `success` and half `fail`, each with a written reason. |
|
|
| **No overlap between train and test.** The 2,738 test tasks that also had a training record were removed from train, and |
| so were 49 more records that show the same photo as a test task (same Open Images ID, same source file, same OmniEdit |
| scene, or a 256-bit image-hash distance of 25 or less). |
|
|
| All images are 1024×768 PNG and are embedded in the Parquet files. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| train = load_dataset("Overdog/MFRL_Data", split="train") |
| test = load_dataset("Overdog/MFRL_Data", split="test") |
| judge = load_dataset("Overdog/MFRL_Data", "judge", split="test") |
| |
| r = train[0] |
| r["source_image"], r["target_image"], r["failed_image"] # PIL images |
| r["instruction"], r["feedback"] |
| ``` |
|
|
| ## Fields (default config) |
|
|
| | Field | Type | Meaning | |
| |---|---|---| |
| | `id` | string | Record ID; the upstream sample it was built from | |
| | `edit_type` | string | One of the 9 content-edit classes | |
| | `instruction` | string | Edit instruction given to the editor | |
| | `source_image` | image (PNG) | Image to edit | |
| | `target_image` | image (PNG) | Ground-truth edit result | |
| | `failed_image` | image (PNG) | Edit result with an obvious error | |
| | `feedback` | string | Problem in the failed image, then the corrective instruction (at most 150 words) | |
| | `feedback_problem` | string | First part of `feedback`: what is wrong and where | |
| | `feedback_correction` | string | Second part of `feedback`: what the editor should do instead | |
| | `failed_image_origin` | string | `original_candidate` (kept from the candidate pool that produced `target_image`) or `regenerated` (one of three new samples; the model is in `failed_image_model`) | |
| | `failed_image_model` | string | Model that generated `failed_image` | |
| | `failed_image_seed` | int64 | Seed of `failed_image` | |
| | `target_image_model` | string | Model that generated `target_image` | |
| | `target_human_confirmed` | bool | Whether a person confirmed `target_image` (otherwise chosen by an automatic judge) | |
| | `feedback_model` | string | Model that judged `failed_image` and wrote `feedback` | |
| | `source_dataset` | string | Dataset the source image and instruction come from | |
| | `source_subset` | string | Subset within `source_dataset` | |
| | `image_key` | string | Source image ID; split train/test by this column | |
| | `split_origin` | string | `ours` (train, or a test task with our quad) or `mfrl_bench` (test task without a quad) | |
| | `mfrl_id` | string | MFRL-Bench task id (test split only) | |
| | `mfrl_category` | string | MFRL-Bench category (test split only; the 4 text classes are one category, `Text`) | |
|
|
| ## Fields (judge config) |
|
|
| | Field | Type | Meaning | |
| |---|---|---| |
| | `id` | string | MFRL-Bench judge item id | |
| | `edit_id` | string | MFRL-Bench edit task the image was made for | |
| | `category` | string | MFRL-Bench category | |
| | `subcategory` | string | Text sub-type, else null | |
| | `instruction` | string | Edit instruction | |
| | `source_image` | image (PNG) | Image to edit | |
| | `candidate_image` | image (PNG) | Edited image to judge | |
| | `label` | string | `success` or `fail` | |
| | `reason` | string | Labeling judge reason (Claude Haiku 5.5) | |
| | `confidence` | string | Labeling judge confidence | |
| | `generator` | string | Model that produced the candidate | |
| | `label_source` | string | How the label was obtained | |
|
|
| ## Counts |
|
|
| Train by edit type: |
|
|
| | Edit type | Records | |
| |---|---:| |
| | Change Object Appearance | 21,753 | |
| | Replace Object | 8,646 | |
| | Add Object | 7,648 | |
| | Remove Object | 4,919 | |
| | Complex Edit | 2,595 | |
| | Add Text | 1,925 | |
| | Move Object | 1,405 | |
| | Replace Text | 942 | |
| | Change Text Style | 465 | |
| | Remove Text | 344 | |
|
|
| Test by MFRL-Bench category: |
|
|
| | Category | Tasks | |
| |---|---:| |
| | Add Object | 500 | |
| | Change Object Appearance | 500 | |
| | Complex Edit | 500 | |
| | Remove Object | 500 | |
| | Replace Object | 500 | |
| | Text | 400 | |
| | Move Object | 100 | |
|
|
| Train by model of the failed image: |
|
|
| | Model | Records | |
| |---|---:| |
| | FLUX.2-klein-9B | 30,923 | |
| | FLUX.2-klein-4B | 9,393 | |
| | Qwen-Image-Edit-2511-Lightning | 5,088 | |
| | FLUX.3 | 5,059 | |
| | Qwen-Image-2.1 | 179 | |
|
|
| Train by source dataset: |
|
|
| | Source | Records | |
| |---|---:| |
| | GPT-Image-Edit-1.5M | 37,602 | |
| | Pico-Banana-400K | 8,753 | |
| | Open Images (TextOCR) | 3,244 | |
| | Open Images | 1,043 | |
|
|
| ## How it was built |
|
|
| 1. **Sources and ground truth.** Source images and instructions come from GPT-Image-Edit-1.5M (OmniEdit subset), |
| Pico-Banana-400K and Open Images V6 (with TextOCR for text edits). Several candidate edits were generated per source |
| with FLUX.3 and Qwen-Image-2.1, and an automatic judge (Claude Haiku 5.5) chose a successful one as the target. |
| 2. **Failed image.** If a failed candidate from that pool had an obvious error, meaning one a person notices within |
| seconds at normal size, it was kept. Otherwise FLUX.2-klein-9B (4 steps) generated three new results and the judge |
| picked the one with the most obvious error. If none had one, FLUX.2-klein-4B (4 steps) tried three more, and then |
| Qwen-Image-Edit-2511 with the Lightning 4-step LoRA. If all nine had no obvious error, the record was dropped. |
| Small shifts in color, tone or sharpness, and differences of taste on style edits, do not count as errors. |
| 3. **Feedback.** The same judge call wrote the feedback: the problem first, then an imperative correction, in words |
| rather than coordinates, at most 150 words. The judge was Claude Haiku 5.5 for most records and Claude Opus 5.5 for |
| part of the text, move and complex-edit records; `feedback_model` names it per record. |
| 4. **Test set.** MFRL-Bench tasks were selected where two independent Claude Haiku 5.5 judgments agreed on every |
| candidate and on the best one, which is the reference. No Pico-Banana-400K image is in the test set. |
|
|
| ## Limitations |
|
|
| - **Labels are automatic.** No person reviewed the failed images, the feedback, the references or the judge labels. |
| - **Generators differ.** In train, most failed images come from FLUX.2-klein, while targets come from FLUX.3. A model |
| trained to tell them apart can learn the generator instead (`failed_image_model`, `target_image_model`). |
| - **Selection bias.** Records where every tried model got the edit right are missing, so easy edits are underrepresented. |
| - **The reference is one valid result**, not the only one. |
|
|
| ## License |
|
|
| The data combines material under different terms; see `LICENSE.md`. In particular, records with |
| `source_dataset = Pico-Banana-400K` are under CC BY-NC-ND 4.0 (non-commercial, no derivatives). All `target_image`, |
| `failed_image` and `candidate_image` files are AI-generated. |
|
|
