DanteLiu/deimv2-x-pill-android

DEIMv2-X Pill + Needle Detector (Android ONNX, fp16)

A fine-tuned DEIMv2-X object detection model for counting pills and insulin pen needle caps. Exported to ONNX with fp16 weights for efficient Android deployment via ONNX Runtime.

Model Details

Performance

Files

Encrypted Model

The .onnx.enc file is encrypted with AES-256-CTR to prevent direct extraction from the APK.

• —Format: [16-byte IV][ciphertext]
• —Decryption: Handled by ModelDecryptor.kt in the Android app at runtime

Quick Start (Android / Kotlin)

Gradle

dependencies {
    implementation 'com.microsoft.onnxruntime:onnxruntime-android:1.21.0'
}

Preprocessing

1. Resize image to 640 x 640 (stretch, no padding)
2. Scale pixels to [0, 1] (divide by 255)
3. Normalize: (pixel / 255.0 - mean) / std
   - mean = [0.485, 0.456, 0.406]
   - std  = [0.229, 0.224, 0.225]
4. Layout: NCHW, convert to float16

Inference

val ortEnv = OrtEnvironment.getEnvironment()
val session = ortEnv.createSession(modelFilePath, OrtSession.SessionOptions())
val results = session.run(mapOf("image" to inputTensor))

Postprocessing

For each of the 300 detections:
  1. Skip if scores[0][i] <= 0.5
  2. Get label: labels[0][i]  (0 = pill, 1 = needle)
  3. Get box: [x1, y1, x2, y2] = boxes[0][i]  (640x640 space)
  4. Scale to original image:
       x_orig = x / 640.0 * original_width
       y_orig = y / 640.0 * original_height

Model I/O

Input:

Outputs:

Key Notes

• —End-to-end: DEIMv2 outputs are final — no NMS or post-processing needed
• —fp16 I/O: Use ShortBuffer + OnnxJavaType.FLOAT16 on Android
• —300 queries: Always outputs 300 candidates; most have near-zero scores
• —Stream from file: Load model from file path (not readBytes()) to avoid OOM on memory-constrained devices

Known Limitations

1. 1.Blister packs: Reflective foil may cause +1–2 overcounting
2. 2.Very small pills: May be missed if too small relative to image — move camera closer
3. 3.Stacked pills: 3+ layers of stacking may cause occlusion
4. 4.Needle types: Trained on insulin pen needle caps only

Training Data

• —333 pill images + 60 needle images
• —Custom dataset, not publicly available

Citation

@misc{reveliorx2026,
  title={RevelioRX: DEIMv2-X Pill and Needle Detector},
  author={Tsung-Han Liu},
  year={2026},
  url={https://huggingface.co/DanteLiu/deimv2-x-pill-android}
}