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