squirelmail/model-BotDetect-CAPTCHA-Generator
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).
📁 Contents -----------
captcha_best.weights.h5— best validation loss (auto-updated during training).captcha_epNNN.weights.h5— 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).
✅ 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📦 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.
🧪 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 checkmodel.py \ --weights /workspace/captchafinal.weights.h5 \ --image /workspace/dataset_500/style7/K9NO2.png
Optional ground truth override:
python3 checkmodel.py \ --weights /workspace/captchafinal.weights.h5 \ --image /workspace/dataset_500/style7/K9NO2.png \ --gt K9NO2
2) Batch from a dataset
python3 checkmodel.py \ --weights /home/infra/models/captchaep002.weights.h5 \ --data-root /datasets/dataset_500 \ --samples 64
Expected directory layout for --data-root:
/datasets/dataset_500/ ├── style0/ │ ├── A1B2C.png │ └── ... ├── style1/ │ └── ... └── ... └── 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: 3× (Conv2D + BN + MaxPool), then reshape to time-steps.
- RNN head: 2× BiLSTM(128),
return_sequences=True. - Classifier: Dense(
num_classes = 36 + 1) with softmax;+1is the CTC blank. - Time steps: width is downsampled by 8 ⇒
250/8 = 31time 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.
🎛️ CLI Options ---------------
--weights <path> : required, *.weights.h5 (same architecture) --image <path> : test a single image --gt <text> : ground truth for --image (default: file name) --data-root <dir> : 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)
📊 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 ------------------
- “A total of 1 objects could not be loaded… <Dense name=predictions>” 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.listphysicaldevices('GPU'))
- NaN loss during training Check: label regex filtering, correct
input_length=31, useint32for CTC inputs, disable LSTM dropouts when using cuDNN (set to0.0).
🔐 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
.kerasafter loading weights in the same environment:
modelwithctc, basemodel = buildmodels(...) modelwithctc.loadweights("captchaepXXX.weights.h5") basemodel.save("captchaepXXX_base.keras")