Upload trained ASL Transformer 84-class 408D model

README.md

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+---
+license: other
+library_name: pytorch
+tags:
+- asl
+- sign-language
+- transformer
+- mediapipe
+- holistic
+- keypoints
+- pytorch
+- isolated-sign-recognition
+- 84-classes
+- 408d-features
+---
+
+# ASL Transformer 84-Class 408D Model
+
+This repository contains a trained PyTorch Transformer model for isolated American Sign Language classification using MediaPipe Holistic keypoint features.
+
+## Model Performance
+
+- Validation Top-1 Accuracy: **72.87%**
+- Validation Top-5 Accuracy: **90.15%**
+- Number of classes: **84**
+- Input sequence length: **50**
+- Input feature dimension: **408**
+
+## Important Files
+
+- `best_transformer_asl_84class.pt` — best trained checkpoint
+- `last_transformer_asl_84class.pt` — final checkpoint from training
+- `config.json` — model/training configuration
+- `id_to_label.json` — class ID to label mapping
+- `training_history.csv` — epoch-by-epoch training history
+- `classification_report.csv` — validation classification report
+- `confusion_matrix.npy` — validation confusion matrix
+- `training_summary.json` — final training summary
+
+## Input Format
+
+The model expects input shaped:
+
+```python
+(batch_size, 50, 408)
+```
+
+Where:
+
+- `50` = sequence length
+- `408` = 204 normalized keypoint features + 204 velocity features
+
+## Training Summary
+
+```json
+{
+  "best_epoch": 60,
+  "best_val_top1": 0.7287202392305646,
+  "final_val_loss": 1.4968034369604928,
+  "final_val_top1": 0.7287202392305646,
+  "final_val_top5": 0.9014880997794015,
+  "total_training_time_sec": 265.57665967941284,
+  "total_training_time": "04m 25s",
+  "best_checkpoint": "/kaggle/working/asl_transformer_84class_run/best_transformer_asl_84class.pt",
+  "last_checkpoint": "/kaggle/working/asl_transformer_84class_run/last_transformer_asl_84class.pt"
+}
+```
+
+## Config
+
+```json
+{
+  "data_dir": "/kaggle/working/training_model23_final_train_ready",
+  "output_dir": "/kaggle/working/asl_transformer_84class_run",
+  "seq_len": 50,
+  "input_dim": 408,
+  "num_classes": 84,
+  "d_model": 256,
+  "nhead": 4,
+  "num_layers": 3,
+  "dim_feedforward": 512,
+  "dropout": 0.3,
+  "batch_size": 64,
+  "epochs": 60,
+  "max_lr": 0.0006,
+  "weight_decay": 0.0001,
+  "label_smoothing": 0.05,
+  "grad_clip": 1.0,
+  "patience": 10,
+  "num_workers": 2,
+  "pin_memory": true,
+  "use_augmentation": true,
+  "noise_std": 0.01,
+  "feature_dropout_prob": 0.03,
+  "time_mask_prob": 0.15,
+  "time_mask_max_len": 6,
+  "seed": 42,
+  "use_amp": true
+}
+```
+
+## Notes
+
+During inference, use the same preprocessing pipeline used during training:
+
+1. Extract MediaPipe Holistic keypoints.
+2. Normalize keypoints the same way as training.
+3. Build a 50-frame sequence.
+4. Add velocity features to convert 204D input into 408D input.
+5. Feed tensor shaped `(1, 50, 408)` into the model.
+6. Convert predicted class ID using `id_to_label.json`.

best_transformer_asl_84class.pt

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best_val_preds.npy

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best_val_targets.npy

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config.json

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+  "data_dir": "/kaggle/working/training_model23_final_train_ready",
+  "output_dir": "/kaggle/working/asl_transformer_84class_run",
+  "seq_len": 50,
+  "input_dim": 408,
+  "num_classes": 84,
+  "d_model": 256,
+  "nhead": 4,
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+  "time_mask_max_len": 6,
+  "seed": 42,
+  "use_amp": true
+}

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training_summary.json

@@ -0,0 +1,11 @@
+{
+  "best_epoch": 60,
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+  "total_training_time": "04m 25s",
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+  "last_checkpoint": "/kaggle/working/asl_transformer_84class_run/last_transformer_asl_84class.pt"
+}