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--- |
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license: apache-2.0 |
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datasets: |
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- squirelmail/dataset-BotDetect-CAPTCHA-Generator |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: image-text-to-text |
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library_name: keras |
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tags: |
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- ocr |
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- captcha |
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- crnn |
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- ctc |
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- tensorflow |
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- keras |
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- 50x250 |
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- uppercase |
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- digits |
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--- |
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# Model AI For Solve BotDetect-CAPTCHA-Generator Gov ID Captcha |
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🧠 CRNN+CTC Checkpoints |
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======================= |
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This directory contains **Keras 3** `save_weights`\-style checkpoints produced during training of a CRNN + CTC model for 5-char uppercase/digit CAPTCHA (image size `H=50`, `W=250`, grayscale). |
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* * * |
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📁 Contents |
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----------- |
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* `captcha_best.weights.h5` — best validation loss (auto-updated during training). |
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* `captcha_epNNN.weights.h5` — per-epoch snapshots (e.g., `captcha_ep001.weights.h5` … `captcha_ep022.weights.h5`). |
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All files are _weights only_; they must be loaded into the same model architecture used in training (the tester builds that architecture for you). |
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* * * |
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✅ Model Result captcha_ep022.weights.h5 => 90.91% Accuracy |
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----------- |
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``` |
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(venv) root@prod-exploit-sa-all-01:/home/infra# date && python3 cek_model_v6.py --weights captcha_ep022.weights.h5 --data-root ./dataset_1000_rand --sample 24000 && date |
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Thu Oct 30 01:12:49 WITA 2025 |
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WARNING: All log messages before absl::InitializeLog() is called are written to STDERR |
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I0000 00:00:1761761571.108235 2264160 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. |
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I0000 00:00:1761761571.304280 2264160 cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. |
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To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. |
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WARNING: All log messages before absl::InitializeLog() is called are written to STDERR |
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I0000 00:00:1761761575.452128 2264160 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. |
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Found weights: captcha_ep022.weights.h5 | size: 27757.0 KB | mtime: Thu Oct 30 01:02:51 2025 |
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E0000 00:00:1761761576.513960 2264160 cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303) |
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TF GPUs: [] |
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OK: weights loaded. |
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Base output shape: (None, 31, 37) |
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Testing on 24000 samples from ./dataset_1000_rand ... |
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W0000 00:00:1761761611.159498 2264160 cpu_allocator_impl.cc:84] Allocation of 1200000000 exceeds 10% of free system memory. |
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00 GT: 976VF | Pred: 976VF |
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01 GT: 7W20H | Pred: 7W20H |
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02 GT: UUU24 | Pred: UUU24 |
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03 GT: 1EMVZ | Pred: 1EMVZ |
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04 GT: WY4RD | Pred: WY4RD |
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05 GT: 0GNKE | Pred: 0GNKE |
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06 GT: 7Y5TY | Pred: 7Y5TY |
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07 GT: OC8C1 | Pred: OC8C1 |
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08 GT: 5ZIDQ | Pred: 5ZIDQ |
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09 GT: LP8IP | Pred: LP8IP |
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10 GT: AKQ7G | Pred: AKQ7G |
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11 GT: X23QD | Pred: X23QD |
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Exact match: 90.91% | Mean CER: 0.0194 |
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Total images tested: 24000 |
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Thu Oct 30 01:18:07 WITA 2025 |
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``` |
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* * * |
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📦 Requirements |
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--------------- |
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Install from the pinned list in the repo root: |
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# (recommended) fresh virtualenv |
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python3 -m venv venv |
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source venv/bin/activate |
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# install exact deps |
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pip install -r captcha_requirements.txt |
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**Important:** Keras/TensorFlow versions should match what was used during training. If you trained with TF/Keras nightly or dev builds, test in the same environment to avoid weight-loading shape/key mismatches. |
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* * * |
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🧪 How to Test |
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-------------- |
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The tester script re-creates the training graph (CRNN+CTC), loads the selected checkpoint, and runs inference with the _base_ (CTC-free) submodel. |
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### 1) Single image |
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python3 check_model.py \ |
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--weights /workspace/captcha_final.weights.h5 \ |
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--image /workspace/dataset_500/style7/K9NO2.png |
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Optional ground truth override: |
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python3 check_model.py \ |
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--weights /workspace/captcha_final.weights.h5 \ |
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--image /workspace/dataset_500/style7/K9NO2.png \ |
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--gt K9NO2 |
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### 2) Batch from a dataset |
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python3 check_model.py \ |
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--weights /home/infra/models/captcha_ep002.weights.h5 \ |
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--data-root /datasets/dataset_500 \ |
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--samples 64 |
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Expected directory layout for `--data-root`: |
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/datasets/dataset_500/ |
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├── style0/ |
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│ ├── A1B2C.png |
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│ └── ... |
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├── style1/ |
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│ └── ... |
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└── ... |
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└── style59/ |
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**Image format:** grayscale PNG, resized to `50x250` in the script. |
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**Labels:** derived from filename (regex `^[A-Z0-9]{5}$`). |
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* * * |
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🧩 Model Details (for reference) |
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-------------------------------- |
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* Backbone: 3× (Conv2D + BN + MaxPool), then reshape to time-steps. |
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* RNN head: 2× BiLSTM(128), `return_sequences=True`. |
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* Classifier: Dense(`num_classes = 36 + 1`) with softmax; `+1` is the CTC blank. |
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* Time steps: width is downsampled by 8 ⇒ `250/8 = 31` time steps. |
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The tester script internally builds both: `model_with_ctc` (training graph) and `base_model` (inference). It loads weights into the training graph and then uses `base_model` for predictions. |
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* * * |
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🎛️ CLI Options |
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--------------- |
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--weights <path> : required, *.weights.h5 (same architecture) |
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--image <path> : test a single image |
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--gt <text> : ground truth for --image (default: file name) |
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--data-root <dir> : style0..style59 folders for batch testing |
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--samples N : max number of images for batch test (default 64) |
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--height H : input height (default 50) |
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--width W : input width (default 250) |
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--ext png|jpg : image extension for batch (default png) |
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--show K : print K sample predictions (default 12) |
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* * * |
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📊 Output |
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--------- |
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* Per-sample preview lines: `GT: ABC12 | Pred: ABC12` |
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* Aggregate metrics: |
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* **Exact match** (% of predictions exactly equal to GT) |
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* **Mean CER** (character error rate) |
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* * * |
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🧯 Troubleshooting |
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------------------ |
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* **“A total of 1 objects could not be loaded… <Dense name=predictions>”** |
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Mismatch between Keras/TF versions or model definition. Use the same environment and architecture as training. |
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* **GPU not used** |
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Ensure a CUDA-enabled TF build and matching drivers. For server-side issues, test with: |
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import tensorflow as tf |
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print(tf.config.list_physical_devices('GPU')) |
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* **NaN loss during training** |
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Check: label regex filtering, correct `input_length=31`, use `int32` for CTC inputs, disable LSTM dropouts when using cuDNN (set to `0.0`). |
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* * * |
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🔐 Notes |
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-------- |
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* CTC blank ID = `36` (since charset is 36 chars: 0-9 + A-Z). |
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* All checkpoints here are _weights only_; to export a full model, save the base model as `.keras` after loading weights in the same environment: |
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model_with_ctc, base_model = build_models(...) |
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model_with_ctc.load_weights("captcha_epXXX.weights.h5") |
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base_model.save("captcha_epXXX_base.keras") |
