Yolo Tflite Models

yolo26n-seg/LICENSE

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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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yolo26n-seg/README.md

@@ -0,0 +1,63 @@
+# YOLO26n-seg TFLite Conversion
+
+YOLO26n-seg is the nano **instance-segmentation** model from [Ultralytics](https://github.com/ultralytics/ultralytics). It has an **NMS-free** head plus mask prototypes, so it produces two outputs:
+
+- `det`   `[1, 300, 6+32]` — `[x1, y1, x2, y2, conf, class_id, 32 mask coeffs]` per detection
+- `proto` `[1, 32, mh, mw]` — mask prototypes
+
+Box coordinates are in **letterboxed-input pixel space** (`0…640`). The demo maps them back to the original image and reconstructs per-instance masks.
+
+## Prerequisites
+
+### Python environment
+
+The `.pt` → TFLite conversion uses Ultralytics. Python **3.10** was used.
+
+```bash
+conda create -n <env_name> python=3.10 -y
+conda activate <env_name>
+pip install "ultralytics==8.4.90" "torch==2.12.1" "torchvision==0.27.1" \
+            "tensorflow==2.21.0" "ai-edge-litert==2.1.5"
+```
+
+These are the exact versions used to produce the provided `yolo26n-seg.tflite` (Python `3.10.20`). `tensorflow` is required for the TFLite export, and `torchvision` must match `torch` (0.27.1 ↔ 2.12.1).
+
+---
+
+## Conversion of YOLO26n-seg to TFLite
+
+The `yolo26n-seg.pt` checkpoint is downloaded automatically by Ultralytics on first use.
+
+```bash
+# CLI
+yolo export model=yolo26n-seg.pt format=tflite imgsz=640
+```
+
+or equivalently with the Python API:
+
+```python
+from ultralytics import YOLO
+YOLO("yolo26n-seg.pt").export(format="tflite", imgsz=640)
+```
+
+Ultralytics writes the export artifacts to `yolo26n-seg_saved_model/`; the float32 model is `yolo26n-seg_saved_model/yolo26n-seg_float32.tflite`. Copy/rename it to `yolo26n-seg.tflite` (the name the demo expects).
+
+---
+
+## Usage
+
+A demo script is provided to run inference using OpenCV DNN:
+
+```bash
+python demo.py --model yolo26n-seg.tflite --image example_outputs/input.jpg --output example_outputs/yolo26n-seg_output.jpg
+```
+
+`demo.py` uses only OpenCV's dnn module (`cv2.dnn.readNet`). It letterboxes the input to 640x640 (matching `ultralytics.data.augment.LetterBox`), runs the network, decodes the NMS-free `[1, 300, 6+32]` head, and reconstructs instance masks from the `[1, 32, mh, mw]` prototypes — mirroring `ultralytics ops.process_mask` (logits = coeffs @ proto, crop in proto space, bilinear-upsample to the letterboxed input, threshold at 0, then map back to the original image). It prints the detected COCO classes / confidences / bounding boxes and saves an annotated image with boxes and colored instance masks.
+
+On the provided `example_outputs/input.jpg` it detects and masks the `person` and the `sports ball`.
+
+---
+
+## License
+
+YOLO26n-seg weights and the Ultralytics exporter are released under **AGPL-3.0** — see the [Ultralytics LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). Any redistribution of the converted `yolo26n-seg.tflite` is subject to those terms.

yolo26n-seg/demo.py

@@ -0,0 +1,164 @@
+"""
+Demo script for YOLO26n-seg (instance segmentation) TFLite model using OpenCV DNN.
+
+Output format (NMS-free seg head):
+  - det   [1, 300, 6+32]  ->  [x1, y1, x2, y2, conf, class_id, 32 mask coeffs]
+  - proto [1, 32, mh, mw] ->  mask prototypes
+
+Mask pipeline mirrors ultralytics ops.process_mask: logits = coeffs @ proto, crop in
+proto space, bilinear-upsample to the letterboxed input, threshold at 0 (== sigmoid>0.5),
+then map back to the original image.
+
+Default input size: 640x640 (letterboxed, matching ultralytics.data.augment.LetterBox).
+
+Usage:
+  python demo.py --model yolo26n-seg.tflite --image example_outputs/input.jpg --output example_outputs/yolo26n-seg_output.jpg
+"""
+
+import argparse
+import cv2
+import numpy as np
+
+
+COCO = [
+    "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+    "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
+    "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+    "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
+    "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
+]
+
+PAD_COLOR = (114, 114, 114)
+
+
+# Vivid, high-contrast BGR palette; one distinct color per detected instance.
+PALETTE = [
+    (56, 56, 255), (31, 112, 255), (29, 178, 255), (49, 210, 207), (10, 249, 72),
+    (23, 204, 146), (134, 219, 61), (255, 194, 0), (255, 115, 100), (236, 24, 0),
+    (255, 56, 132), (255, 56, 203), (199, 55, 255), (147, 69, 52), (52, 147, 26),
+]
+
+
+def color_for(i):
+    return PALETTE[i % len(PALETTE)]
+
+
+def letterbox(img, new_h, new_w):
+    """Aspect-preserving resize + centered pad (Ultralytics LetterBox defaults, pad=114)."""
+    h0, w0 = img.shape[:2]
+    ratio = min(new_h / h0, new_w / w0)
+    unpad_w, unpad_h = round(w0 * ratio), round(h0 * ratio)
+    dw, dh = (new_w - unpad_w) / 2.0, (new_h - unpad_h) / 2.0
+    top, bottom = round(dh - 0.1), round(dh + 0.1)
+    left, right = round(dw - 0.1), round(dw + 0.1)
+    if (unpad_w, unpad_h) != (w0, h0):
+        img = cv2.resize(img, (unpad_w, unpad_h), interpolation=cv2.INTER_LINEAR)
+    padded = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=PAD_COLOR)
+    return padded, ratio, left, top
+
+
+def unletterbox(x, y, ratio, pad_x, pad_y):
+    return (x - pad_x) / ratio, (y - pad_y) / ratio
+
+
+def decode_seg_head(det, ratio, pad_x, pad_y, conf_thresh):
+    """det: (1, 300, 6+nc) -> boxes (orig px), scores, class ids, letterbox-space xyxy, coeffs."""
+    rows = det.reshape(det.shape[1], -1)
+    keep = rows[:, 4] >= conf_thresh
+    rows = rows[keep]
+
+    boxes, scores, class_ids, boxes_lb = [], [], [], []
+    for r in rows:
+        x1, y1 = unletterbox(r[0], r[1], ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(r[2], r[3], ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+        scores.append(float(r[4]))
+        class_ids.append(int(r[5]))
+        boxes_lb.append([r[0], r[1], r[2], r[3]])
+    coeffs = rows[:, 6:].astype(np.float32) if len(rows) else np.zeros((0, 0), np.float32)
+    return boxes, scores, class_ids, boxes_lb, coeffs
+
+
+def process_masks(proto, coeffs, boxes_lb, net_w, net_h, pad_x, pad_y, ratio, orig_h, orig_w):
+    """Mirrors ultralytics ops.process_mask + crop_mask."""
+    _, C, mh, mw = proto.shape
+    proto2d = proto.reshape(C, mh * mw)
+    logits = coeffs @ proto2d                       # N x (mh*mw)
+    wr, hr = mw / net_w, mh / net_h
+    content_w, content_h = round(orig_w * ratio), round(orig_h * ratio)
+
+    masks = []
+    for i in range(logits.shape[0]):
+        m = logits[i].reshape(mh, mw).copy()
+        bx1, by1 = boxes_lb[i][0] * wr, boxes_lb[i][1] * hr
+        bx2, by2 = boxes_lb[i][2] * wr, boxes_lb[i][3] * hr
+        cols = np.arange(mw)[None, :]
+        rows_ = np.arange(mh)[:, None]
+        crop = (cols >= bx1) & (cols < bx2) & (rows_ >= by1) & (rows_ < by2)
+        m[~crop] = 0.0
+        up = cv2.resize(m, (net_w, net_h), interpolation=cv2.INTER_LINEAR)
+        binm = (up > 0.0).astype(np.uint8) * 255
+        content = binm[pad_y:pad_y + content_h, pad_x:pad_x + content_w]
+        full = cv2.resize(content, (orig_w, orig_h), interpolation=cv2.INTER_NEAREST)
+        masks.append(full)
+    return masks
+
+
+def main():
+    parser = argparse.ArgumentParser(description="YOLO segmentation TFLite demo (OpenCV DNN)")
+    parser.add_argument("--model", required=True, help="Path to TFLite seg model")
+    parser.add_argument("--image", default="example_outputs/input.jpg", help="Path to input image")
+    parser.add_argument("--output", default="output.jpg", help="Path for annotated output")
+    parser.add_argument("--input-size", type=int, default=640, help="Model input size (W=H)")
+    parser.add_argument("--conf", type=float, default=0.55, help="Confidence threshold")
+    args = parser.parse_args()
+
+    img = cv2.imread(args.image)
+    if img is None:
+        raise FileNotFoundError(f"Cannot read image: {args.image}")
+    orig_h, orig_w = img.shape[:2]
+
+    net = cv2.dnn.readNet(args.model)
+
+    imgsz = args.input_size
+    padded, ratio, pad_x, pad_y = letterbox(img, imgsz, imgsz)
+    blob = cv2.dnn.blobFromImage(padded, 1.0 / 255.0, (imgsz, imgsz), swapRB=True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(net.getUnconnectedOutLayersNames())
+    print("outputs:", [o.shape for o in outs])
+
+    det = next((o for o in outs if o.ndim == 3 and o.shape[1] == 300), None)
+    proto = next((o for o in outs if o.ndim == 4), None)
+    if det is None or proto is None or det.shape[2] <= 6:
+        raise RuntimeError("not a seg model (need a (1,300,>6) head and a (1,C,mh,mw) proto)")
+
+    boxes, scores, class_ids, boxes_lb, coeffs = decode_seg_head(det, ratio, pad_x, pad_y, args.conf)
+    masks = process_masks(proto, coeffs, boxes_lb, imgsz, imgsz, pad_x, pad_y, ratio, orig_h, orig_w)
+
+    out = img.copy()
+    for k, mask in enumerate(masks):
+        color = np.array(color_for(k), np.uint8)
+        blended = cv2.addWeighted(out, 0.5, np.full_like(out, color), 0.5, 0)
+        out[mask > 0] = blended[mask > 0]
+    for k, (x, y, w, h) in enumerate(boxes):
+        color = color_for(k)
+        p1, p2 = (int(x), int(y)), (int(x + w), int(y + h))
+        cls = COCO[class_ids[k]] if 0 <= class_ids[k] < 80 else str(class_ids[k])
+        label = f"{cls} {scores[k]:.2f}"
+        cv2.rectangle(out, p1, p2, color, 2)
+        (tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
+        ly = max(th + 6, p1[1])
+        cv2.rectangle(out, (p1[0], ly - th - 6), (p1[0] + tw + 4, ly), color, -1)
+        cv2.putText(out, label, (p1[0] + 2, ly - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+        print(f"  {label} box=[{p1[0]},{p1[1]},{p2[0]},{p2[1]}]")
+
+    print(f"{len(boxes)} detections above conf {args.conf}")
+    cv2.imwrite(args.output, out)
+    print(f"Saved annotated image: {args.output}")
+
+
+if __name__ == "__main__":
+    main()

yolo26n-seg/yolo26n-seg.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d0fa77e6e5a3fe933df15ffbe770a69d69095f20c4d6f47441323201ce2871b
+size 11687857

yolo26n/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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yolo26n/README.md

@@ -0,0 +1,60 @@
+# YOLO26n TFLite Conversion
+
+YOLO26n is the nano detection model from [Ultralytics](https://github.com/ultralytics/ultralytics). It has an **NMS-free** ("end-to-end") head, so its output is a single `[1, 300, 6]` tensor of already-decoded detections — `[x1, y1, x2, y2, conf, class_id]` per row — and no post-NMS step is needed.
+
+The box coordinates are in **letterboxed-input pixel space** (`0…640`), which the demo maps back to the original image.
+
+## Prerequisites
+
+### Python environment
+
+The `.pt` → TFLite conversion uses Ultralytics. Python **3.10** was used.
+
+```bash
+conda create -n <env_name> python=3.10 -y
+conda activate <env_name>
+pip install "ultralytics==8.4.90" "torch==2.12.1" "torchvision==0.27.1" \
+            "tensorflow==2.21.0" "ai-edge-litert==2.1.5"
+```
+
+These are the exact versions used to produce the provided `yolo26n.tflite` (Python `3.10.20`). `tensorflow` is required for the TFLite export, and `torchvision` must match `torch` (0.27.1 ↔ 2.12.1).
+
+---
+
+## Conversion of YOLO26n to TFLite
+
+The `yolo26n.pt` checkpoint is downloaded automatically by Ultralytics on first use.
+
+```bash
+# CLI
+yolo export model=yolo26n.pt format=tflite imgsz=640
+```
+
+or equivalently with the Python API:
+
+```python
+from ultralytics import YOLO
+YOLO("yolo26n.pt").export(format="tflite", imgsz=640)
+```
+
+Ultralytics writes the export artifacts to `yolo26n_saved_model/`; the float32 model is `yolo26n_saved_model/yolo26n_float32.tflite`. Copy/rename it to `yolo26n.tflite` (the name the demo expects).
+
+---
+
+## Usage
+
+A demo script is provided to run inference using OpenCV DNN:
+
+```bash
+python demo.py --model yolo26n.tflite --image example_outputs/input.jpg --output example_outputs/yolo26n_output.jpg
+```
+
+`demo.py` uses only OpenCV's dnn module (`cv2.dnn.readNet`). It letterboxes the input to 640x640 (aspect-preserving resize + centered 114-pad, matching `ultralytics.data.augment.LetterBox`), runs the network, decodes the NMS-free `[1, 300, 6]` head (each row is a final detection; boxes mapped back through the inverse letterbox — no post-NMS needed), prints the detected COCO classes / confidences / bounding boxes, and saves an annotated output image. It also handles the classic (v5/v8-style) `[1, 4+nc, num_anchors]` head, so the same script works for other exported YOLO `.tflite` models.
+
+On the provided `example_outputs/input.jpg` it detects `bicycle`, `dog` and `truck`.
+
+---
+
+## License
+
+YOLO26n weights and the Ultralytics exporter are released under **AGPL-3.0** — see the [Ultralytics LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). Any redistribution of the converted `yolo26n.tflite` is subject to those terms.

yolo26n/demo.py

@@ -0,0 +1,142 @@
+"""
+Demo script for YOLO26n TFLite model using OpenCV DNN.
+
+Output format:
+  - single tensor [1, 300, 6]  ->  [x1, y1, x2, y2, conf, class_id] per detection
+                                   (NMS-free head, already decoded, no post-NMS needed)
+
+Default input size: 640x640 (letterboxed, matching ultralytics.data.augment.LetterBox).
+
+Also handles the classic (v5/v8) [1, 4+nc, num_anchors] head, so the same script works
+for other exported YOLO .tflite models.
+
+Usage:
+  python demo.py --model yolo26n.tflite --image example_outputs/input.jpg --output example_outputs/yolo26n_output.jpg
+"""
+
+import argparse
+import cv2
+import numpy as np
+
+
+COCO = [
+    "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+    "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
+    "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+    "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
+    "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
+]
+
+PAD_COLOR = (114, 114, 114)
+
+
+def letterbox(img, new_h, new_w):
+    """Aspect-preserving resize + centered pad (Ultralytics LetterBox defaults, pad=114)."""
+    h0, w0 = img.shape[:2]
+    ratio = min(new_h / h0, new_w / w0)
+    unpad_w, unpad_h = round(w0 * ratio), round(h0 * ratio)
+    dw, dh = (new_w - unpad_w) / 2.0, (new_h - unpad_h) / 2.0
+    top, bottom = round(dh - 0.1), round(dh + 0.1)
+    left, right = round(dw - 0.1), round(dw + 0.1)
+    if (unpad_w, unpad_h) != (w0, h0):
+        img = cv2.resize(img, (unpad_w, unpad_h), interpolation=cv2.INTER_LINEAR)
+    padded = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=PAD_COLOR)
+    return padded, ratio, left, top
+
+
+def unletterbox(x, y, ratio, pad_x, pad_y):
+    return (x - pad_x) / ratio, (y - pad_y) / ratio
+
+
+def is_nms_free_head(shape):
+    # v10/26-style heads emit a fixed 300 already-decoded detections with a small row width.
+    return len(shape) == 3 and shape[1] == 300 and shape[2] < shape[1]
+
+
+def decode_classic_head(out, net_w, net_h, ratio, pad_x, pad_y, conf_thresh):
+    # out: (1, 4+nc, num_anchors) -> (num_anchors, 4+nc)
+    pred = out.reshape(out.shape[1], -1).T
+    cls_scores = pred[:, 4:]
+    class_ids = np.argmax(cls_scores, axis=1)
+    confs = cls_scores[np.arange(cls_scores.shape[0]), class_ids]
+    keep = confs >= conf_thresh
+    pred, class_ids, confs = pred[keep], class_ids[keep], confs[keep]
+
+    boxes = []
+    for row in pred:
+        # normalized [0,1] of the letterboxed input -> original-image pixels
+        cx, cy, bw, bh = row[0] * net_w, row[1] * net_h, row[2] * net_w, row[3] * net_h
+        x1, y1 = unletterbox(cx - bw / 2.0, cy - bh / 2.0, ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(cx + bw / 2.0, cy + bh / 2.0, ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+    return boxes, confs.tolist(), class_ids.tolist()
+
+
+def decode_nms_free_head(out, ratio, pad_x, pad_y, conf_thresh):
+    # out: (1, 300, 6+) -- [x1, y1, x2, y2, conf, class_id, ...]; coords in letterboxed pixels
+    dets = out.reshape(out.shape[1], -1)
+    boxes, scores, class_ids = [], [], []
+    for row in dets:
+        if row[4] < conf_thresh:
+            continue
+        x1, y1 = unletterbox(row[0], row[1], ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(row[2], row[3], ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+        scores.append(float(row[4]))
+        class_ids.append(int(row[5]))
+    return boxes, scores, class_ids
+
+
+def main():
+    parser = argparse.ArgumentParser(description="YOLO TFLite demo (OpenCV DNN)")
+    parser.add_argument("--model", required=True, help="Path to TFLite model")
+    parser.add_argument("--image", default="example_outputs/input.jpg", help="Path to input image")
+    parser.add_argument("--output", default="output.jpg", help="Path for annotated output")
+    parser.add_argument("--input-size", type=int, default=640, help="Model input size (W=H)")
+    parser.add_argument("--conf", type=float, default=0.5, help="Confidence threshold")
+    parser.add_argument("--nms", type=float, default=0.45, help="NMS IoU threshold")
+    args = parser.parse_args()
+
+    img = cv2.imread(args.image)
+    if img is None:
+        raise FileNotFoundError(f"Cannot read image: {args.image}")
+
+    net = cv2.dnn.readNet(args.model)
+
+    imgsz = args.input_size
+    padded, ratio, pad_x, pad_y = letterbox(img, imgsz, imgsz)
+    blob = cv2.dnn.blobFromImage(padded, 1.0 / 255.0, (imgsz, imgsz), swapRB=True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(net.getUnconnectedOutLayersNames())
+    print("outputs:", [o.shape for o in outs])
+
+    # Pick the detection head: the output with the largest last dimension.
+    det = max(outs, key=lambda o: o.shape[-1] if o.ndim == 3 else -1)
+
+    if is_nms_free_head(det.shape):
+        boxes, scores, class_ids = decode_nms_free_head(det, ratio, pad_x, pad_y, args.conf)
+        keep = list(range(len(boxes)))
+    else:
+        boxes, scores, class_ids = decode_classic_head(det, imgsz, imgsz, ratio, pad_x, pad_y, args.conf)
+        keep = cv2.dnn.NMSBoxesBatched(boxes, scores, class_ids, args.conf, args.nms)
+        keep = np.array(keep).flatten().tolist()
+
+    out = img.copy()
+    for idx in keep:
+        x, y, w, h = (int(v) for v in boxes[idx])
+        cls = COCO[class_ids[idx]] if 0 <= class_ids[idx] < 80 else str(class_ids[idx])
+        label = f"{cls} {scores[idx]:.2f}"
+        cv2.rectangle(out, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(out, label, (x, max(15, y - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+        print(f"  {label} box=[{x},{y},{w},{h}]")
+
+    print(f"{len(keep)} detections above conf {args.conf}")
+    cv2.imwrite(args.output, out)
+    print(f"Saved annotated image: {args.output}")
+
+
+if __name__ == "__main__":
+    main()

yolo26n/yolo26n.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73cfa2b590580be36c3d0b4f606fca9459dc1399e626e9104d9e45ab601d91ff
+size 10274278

yolov5nu/LICENSE

@@ -0,0 +1,661 @@
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yolov5nu/README.md

@@ -0,0 +1,60 @@
+# YOLOv5nu TFLite Conversion
+
+YOLOv5nu is the anchor-free ("u") variant of YOLOv5 from [Ultralytics](https://github.com/ultralytics/ultralytics). It uses the YOLOv8-style decoupled, anchor-free detection head, so its output is a single `[1, 84, 8400]` tensor (`84 = 4` box coordinates `+ 80` COCO class scores, no separate objectness channel).
+
+The exported TFLite outputs box coordinates **normalized to `[0, 1]`** (the demo's post-processing accounts for this).
+
+## Prerequisites
+
+### Python environment
+
+The `.pt` → TFLite conversion uses Ultralytics. Python **3.10** was used.
+
+```bash
+conda create -n <env_name> python=3.10 -y
+conda activate <env_name>
+pip install "ultralytics==8.4.90" "torch==2.12.1" "torchvision==0.27.1" \
+            "tensorflow==2.21.0" "ai-edge-litert==2.1.5"
+```
+
+These are the exact versions used to produce the provided `yolov5nu.tflite` (Python `3.10.20`). `tensorflow` is required for the TFLite export, and `torchvision` must match `torch` (0.27.1 ↔ 2.12.1).
+
+---
+
+## Conversion of YOLOv5nu to TFLite
+
+The `yolov5nu.pt` checkpoint is downloaded automatically by Ultralytics on first use.
+
+```bash
+# CLI
+yolo export model=yolov5nu.pt format=tflite imgsz=640
+```
+
+or equivalently with the Python API:
+
+```python
+from ultralytics import YOLO
+YOLO("yolov5nu.pt").export(format="tflite", imgsz=640)
+```
+
+Ultralytics writes the export artifacts to `yolov5nu_saved_model/`; the float32 model is `yolov5nu_saved_model/yolov5nu_float32.tflite`. Copy/rename it to `yolov5nu.tflite` (the name the demo expects).
+
+---
+
+## Usage
+
+A demo script is provided to run inference using OpenCV DNN:
+
+```bash
+python demo.py --model yolov5nu.tflite --image example_outputs/input.jpg --output example_outputs/yolov5nu_output.jpg
+```
+
+`demo.py` uses only OpenCV's dnn module (`cv2.dnn.readNet`). It letterboxes the input to 640x640 (aspect-preserving resize + centered 114-pad, matching `ultralytics.data.augment.LetterBox`), runs the network, decodes the `[1, 84, 8400]` head (argmax over the 80 class scores; boxes are normalized `[0, 1]`, scaled to the letterboxed input and mapped back through the inverse letterbox), applies class-aware NMS (`NMSBoxesBatched`), prints the detected COCO classes / confidences / bounding boxes, and saves an annotated output image. It also handles the NMS-free (v10/26-style) `[1, 300, 6+]` head, so the same script works for other exported YOLO `.tflite` models.
+
+On the provided `example_outputs/input.jpg` it detects `dog`, `truck` and `bicycle`.
+
+---
+
+## License
+
+YOLOv5nu weights and the Ultralytics exporter are released under **AGPL-3.0** — see the [Ultralytics LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). Any redistribution of the converted `yolov5nu.tflite` is subject to those terms.

yolov5nu/demo.py

@@ -0,0 +1,142 @@
+"""
+Demo script for YOLOv5nu TFLite model using OpenCV DNN.
+
+Output format:
+  - single tensor [1, 84, 8400]  ->  84 = 4 box (cx, cy, w, h, normalized [0, 1])
+                                      + 80 COCO class scores (no separate objectness)
+
+Default input size: 640x640 (letterboxed, matching ultralytics.data.augment.LetterBox).
+
+Also handles the NMS-free (v10/26) [1, 300, 6+] head, so the same script works for
+other exported YOLO .tflite models.
+
+Usage:
+  python demo.py --model yolov5nu.tflite --image example_outputs/input.jpg --output example_outputs/yolov5nu_output.jpg
+"""
+
+import argparse
+import cv2
+import numpy as np
+
+
+COCO = [
+    "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+    "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
+    "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+    "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
+    "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
+]
+
+PAD_COLOR = (114, 114, 114)
+
+
+def letterbox(img, new_h, new_w):
+    """Aspect-preserving resize + centered pad (Ultralytics LetterBox defaults, pad=114)."""
+    h0, w0 = img.shape[:2]
+    ratio = min(new_h / h0, new_w / w0)
+    unpad_w, unpad_h = round(w0 * ratio), round(h0 * ratio)
+    dw, dh = (new_w - unpad_w) / 2.0, (new_h - unpad_h) / 2.0
+    top, bottom = round(dh - 0.1), round(dh + 0.1)
+    left, right = round(dw - 0.1), round(dw + 0.1)
+    if (unpad_w, unpad_h) != (w0, h0):
+        img = cv2.resize(img, (unpad_w, unpad_h), interpolation=cv2.INTER_LINEAR)
+    padded = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=PAD_COLOR)
+    return padded, ratio, left, top
+
+
+def unletterbox(x, y, ratio, pad_x, pad_y):
+    return (x - pad_x) / ratio, (y - pad_y) / ratio
+
+
+def is_nms_free_head(shape):
+    # v10/26-style heads emit a fixed 300 already-decoded detections with a small row width.
+    return len(shape) == 3 and shape[1] == 300 and shape[2] < shape[1]
+
+
+def decode_classic_head(out, net_w, net_h, ratio, pad_x, pad_y, conf_thresh):
+    # out: (1, 4+nc, num_anchors) -> (num_anchors, 4+nc)
+    pred = out.reshape(out.shape[1], -1).T
+    cls_scores = pred[:, 4:]
+    class_ids = np.argmax(cls_scores, axis=1)
+    confs = cls_scores[np.arange(cls_scores.shape[0]), class_ids]
+    keep = confs >= conf_thresh
+    pred, class_ids, confs = pred[keep], class_ids[keep], confs[keep]
+
+    boxes = []
+    for row in pred:
+        # normalized [0,1] of the letterboxed input -> original-image pixels
+        cx, cy, bw, bh = row[0] * net_w, row[1] * net_h, row[2] * net_w, row[3] * net_h
+        x1, y1 = unletterbox(cx - bw / 2.0, cy - bh / 2.0, ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(cx + bw / 2.0, cy + bh / 2.0, ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+    return boxes, confs.tolist(), class_ids.tolist()
+
+
+def decode_nms_free_head(out, ratio, pad_x, pad_y, conf_thresh):
+    # out: (1, 300, 6+) -- [x1, y1, x2, y2, conf, class_id, ...]; coords in letterboxed pixels
+    dets = out.reshape(out.shape[1], -1)
+    boxes, scores, class_ids = [], [], []
+    for row in dets:
+        if row[4] < conf_thresh:
+            continue
+        x1, y1 = unletterbox(row[0], row[1], ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(row[2], row[3], ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+        scores.append(float(row[4]))
+        class_ids.append(int(row[5]))
+    return boxes, scores, class_ids
+
+
+def main():
+    parser = argparse.ArgumentParser(description="YOLO TFLite demo (OpenCV DNN)")
+    parser.add_argument("--model", required=True, help="Path to TFLite model")
+    parser.add_argument("--image", default="example_outputs/input.jpg", help="Path to input image")
+    parser.add_argument("--output", default="output.jpg", help="Path for annotated output")
+    parser.add_argument("--input-size", type=int, default=640, help="Model input size (W=H)")
+    parser.add_argument("--conf", type=float, default=0.5, help="Confidence threshold")
+    parser.add_argument("--nms", type=float, default=0.45, help="NMS IoU threshold")
+    args = parser.parse_args()
+
+    img = cv2.imread(args.image)
+    if img is None:
+        raise FileNotFoundError(f"Cannot read image: {args.image}")
+
+    net = cv2.dnn.readNet(args.model)
+
+    imgsz = args.input_size
+    padded, ratio, pad_x, pad_y = letterbox(img, imgsz, imgsz)
+    blob = cv2.dnn.blobFromImage(padded, 1.0 / 255.0, (imgsz, imgsz), swapRB=True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(net.getUnconnectedOutLayersNames())
+    print("outputs:", [o.shape for o in outs])
+
+    # Pick the detection head: the output with the largest last dimension.
+    det = max(outs, key=lambda o: o.shape[-1] if o.ndim == 3 else -1)
+
+    if is_nms_free_head(det.shape):
+        boxes, scores, class_ids = decode_nms_free_head(det, ratio, pad_x, pad_y, args.conf)
+        keep = list(range(len(boxes)))
+    else:
+        boxes, scores, class_ids = decode_classic_head(det, imgsz, imgsz, ratio, pad_x, pad_y, args.conf)
+        keep = cv2.dnn.NMSBoxesBatched(boxes, scores, class_ids, args.conf, args.nms)
+        keep = np.array(keep).flatten().tolist()
+
+    out = img.copy()
+    for idx in keep:
+        x, y, w, h = (int(v) for v in boxes[idx])
+        cls = COCO[class_ids[idx]] if 0 <= class_ids[idx] < 80 else str(class_ids[idx])
+        label = f"{cls} {scores[idx]:.2f}"
+        cv2.rectangle(out, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(out, label, (x, max(15, y - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+        print(f"  {label} box=[{x},{y},{w},{h}]")
+
+    print(f"{len(keep)} detections above conf {args.conf}")
+    cv2.imwrite(args.output, out)
+    print(f"Saved annotated image: {args.output}")
+
+
+if __name__ == "__main__":
+    main()

yolov5nu/yolov5nu.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfa8013d0b5e4ac0ba939405f02d414d189c1fa0e6a4d37c94bf063ce178447a
+size 10848662

yolov8n/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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yolov8n/README.md

@@ -0,0 +1,60 @@
+# YOLOv8n TFLite Conversion
+
+YOLOv8n is the nano detection model from [Ultralytics](https://github.com/ultralytics/ultralytics). It uses the anchor-free, decoupled detection head, so its output is a single `[1, 84, 8400]` tensor (`84 = 4` box coordinates `+ 80` COCO class scores, no separate objectness channel).
+
+The exported TFLite outputs box coordinates **normalized to `[0, 1]`** (the demo's post-processing accounts for this).
+
+## Prerequisites
+
+### Python environment
+
+The `.pt` → TFLite conversion uses Ultralytics. Python **3.10** was used.
+
+```bash
+conda create -n <env_name> python=3.10 -y
+conda activate <env_name>
+pip install "ultralytics==8.4.90" "torch==2.12.1" "torchvision==0.27.1" \
+            "tensorflow==2.21.0" "ai-edge-litert==2.1.5"
+```
+
+These are the exact versions used to produce the provided `yolov8n.tflite` (Python `3.10.20`). `tensorflow` is required for the TFLite export, and `torchvision` must match `torch` (0.27.1 ↔ 2.12.1).
+
+---
+
+## Conversion of YOLOv8n to TFLite
+
+The `yolov8n.pt` checkpoint is downloaded automatically by Ultralytics on first use.
+
+```bash
+# CLI
+yolo export model=yolov8n.pt format=tflite imgsz=640
+```
+
+or equivalently with the Python API:
+
+```python
+from ultralytics import YOLO
+YOLO("yolov8n.pt").export(format="tflite", imgsz=640)
+```
+
+Ultralytics writes the export artifacts to `yolov8n_saved_model/`; the float32 model is `yolov8n_saved_model/yolov8n_float32.tflite`. Copy/rename it to `yolov8n.tflite` (the name the demo expects).
+
+---
+
+## Usage
+
+A demo script is provided to run inference using OpenCV DNN:
+
+```bash
+python demo.py --model yolov8n.tflite --image example_outputs/input.jpg --output example_outputs/yolov8n_output.jpg
+```
+
+`demo.py` uses only OpenCV's dnn module (`cv2.dnn.readNet`). It letterboxes the input to 640x640 (aspect-preserving resize + centered 114-pad, matching `ultralytics.data.augment.LetterBox`), runs the network, decodes the `[1, 84, 8400]` head (argmax over the 80 class scores; boxes are normalized `[0, 1]`, scaled to the letterboxed input and mapped back through the inverse letterbox), applies class-aware NMS (`NMSBoxesBatched`), prints the detected COCO classes / confidences / bounding boxes, and saves an annotated output image. It also handles the NMS-free (v10/26-style) `[1, 300, 6+]` head, so the same script works for other exported YOLO `.tflite` models.
+
+On the provided `example_outputs/input.jpg` it detects `dog`, `bicycle` and `car`.
+
+---
+
+## License
+
+YOLOv8n weights and the Ultralytics exporter are released under **AGPL-3.0** — see the [Ultralytics LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). Any redistribution of the converted `yolov8n.tflite` is subject to those terms.

yolov8n/demo.py

@@ -0,0 +1,142 @@
+"""
+Demo script for YOLOv8n TFLite model using OpenCV DNN.
+
+Output format:
+  - single tensor [1, 84, 8400]  ->  84 = 4 box (cx, cy, w, h, normalized [0, 1])
+                                      + 80 COCO class scores (no separate objectness)
+
+Default input size: 640x640 (letterboxed, matching ultralytics.data.augment.LetterBox).
+
+Also handles the NMS-free (v10/26) [1, 300, 6+] head, so the same script works for
+other exported YOLO .tflite models.
+
+Usage:
+  python demo.py --model yolov8n.tflite --image example_outputs/input.jpg --output example_outputs/yolov8n_output.jpg
+"""
+
+import argparse
+import cv2
+import numpy as np
+
+
+COCO = [
+    "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+    "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
+    "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+    "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
+    "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
+]
+
+PAD_COLOR = (114, 114, 114)
+
+
+def letterbox(img, new_h, new_w):
+    """Aspect-preserving resize + centered pad (Ultralytics LetterBox defaults, pad=114)."""
+    h0, w0 = img.shape[:2]
+    ratio = min(new_h / h0, new_w / w0)
+    unpad_w, unpad_h = round(w0 * ratio), round(h0 * ratio)
+    dw, dh = (new_w - unpad_w) / 2.0, (new_h - unpad_h) / 2.0
+    top, bottom = round(dh - 0.1), round(dh + 0.1)
+    left, right = round(dw - 0.1), round(dw + 0.1)
+    if (unpad_w, unpad_h) != (w0, h0):
+        img = cv2.resize(img, (unpad_w, unpad_h), interpolation=cv2.INTER_LINEAR)
+    padded = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=PAD_COLOR)
+    return padded, ratio, left, top
+
+
+def unletterbox(x, y, ratio, pad_x, pad_y):
+    return (x - pad_x) / ratio, (y - pad_y) / ratio
+
+
+def is_nms_free_head(shape):
+    # v10/26-style heads emit a fixed 300 already-decoded detections with a small row width.
+    return len(shape) == 3 and shape[1] == 300 and shape[2] < shape[1]
+
+
+def decode_classic_head(out, net_w, net_h, ratio, pad_x, pad_y, conf_thresh):
+    # out: (1, 4+nc, num_anchors) -> (num_anchors, 4+nc)
+    pred = out.reshape(out.shape[1], -1).T
+    cls_scores = pred[:, 4:]
+    class_ids = np.argmax(cls_scores, axis=1)
+    confs = cls_scores[np.arange(cls_scores.shape[0]), class_ids]
+    keep = confs >= conf_thresh
+    pred, class_ids, confs = pred[keep], class_ids[keep], confs[keep]
+
+    boxes = []
+    for row in pred:
+        # normalized [0,1] of the letterboxed input -> original-image pixels
+        cx, cy, bw, bh = row[0] * net_w, row[1] * net_h, row[2] * net_w, row[3] * net_h
+        x1, y1 = unletterbox(cx - bw / 2.0, cy - bh / 2.0, ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(cx + bw / 2.0, cy + bh / 2.0, ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+    return boxes, confs.tolist(), class_ids.tolist()
+
+
+def decode_nms_free_head(out, ratio, pad_x, pad_y, conf_thresh):
+    # out: (1, 300, 6+) -- [x1, y1, x2, y2, conf, class_id, ...]; coords in letterboxed pixels
+    dets = out.reshape(out.shape[1], -1)
+    boxes, scores, class_ids = [], [], []
+    for row in dets:
+        if row[4] < conf_thresh:
+            continue
+        x1, y1 = unletterbox(row[0], row[1], ratio, pad_x, pad_y)
+        x2, y2 = unletterbox(row[2], row[3], ratio, pad_x, pad_y)
+        boxes.append([x1, y1, x2 - x1, y2 - y1])
+        scores.append(float(row[4]))
+        class_ids.append(int(row[5]))
+    return boxes, scores, class_ids
+
+
+def main():
+    parser = argparse.ArgumentParser(description="YOLO TFLite demo (OpenCV DNN)")
+    parser.add_argument("--model", required=True, help="Path to TFLite model")
+    parser.add_argument("--image", default="example_outputs/input.jpg", help="Path to input image")
+    parser.add_argument("--output", default="output.jpg", help="Path for annotated output")
+    parser.add_argument("--input-size", type=int, default=640, help="Model input size (W=H)")
+    parser.add_argument("--conf", type=float, default=0.5, help="Confidence threshold")
+    parser.add_argument("--nms", type=float, default=0.45, help="NMS IoU threshold")
+    args = parser.parse_args()
+
+    img = cv2.imread(args.image)
+    if img is None:
+        raise FileNotFoundError(f"Cannot read image: {args.image}")
+
+    net = cv2.dnn.readNet(args.model)
+
+    imgsz = args.input_size
+    padded, ratio, pad_x, pad_y = letterbox(img, imgsz, imgsz)
+    blob = cv2.dnn.blobFromImage(padded, 1.0 / 255.0, (imgsz, imgsz), swapRB=True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(net.getUnconnectedOutLayersNames())
+    print("outputs:", [o.shape for o in outs])
+
+    # Pick the detection head: the output with the largest last dimension.
+    det = max(outs, key=lambda o: o.shape[-1] if o.ndim == 3 else -1)
+
+    if is_nms_free_head(det.shape):
+        boxes, scores, class_ids = decode_nms_free_head(det, ratio, pad_x, pad_y, args.conf)
+        keep = list(range(len(boxes)))
+    else:
+        boxes, scores, class_ids = decode_classic_head(det, imgsz, imgsz, ratio, pad_x, pad_y, args.conf)
+        keep = cv2.dnn.NMSBoxesBatched(boxes, scores, class_ids, args.conf, args.nms)
+        keep = np.array(keep).flatten().tolist()
+
+    out = img.copy()
+    for idx in keep:
+        x, y, w, h = (int(v) for v in boxes[idx])
+        cls = COCO[class_ids[idx]] if 0 <= class_ids[idx] < 80 else str(class_ids[idx])
+        label = f"{cls} {scores[idx]:.2f}"
+        cv2.rectangle(out, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(out, label, (x, max(15, y - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+        print(f"  {label} box=[{x},{y},{w},{h}]")
+
+    print(f"{len(keep)} detections above conf {args.conf}")
+    cv2.imwrite(args.output, out)
+    print(f"Saved annotated image: {args.output}")
+
+
+if __name__ == "__main__":
+    main()

yolov8n/yolov8n.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2dde486ad6ec930ec3be2f9248351baef0ac4b1ad93a083fc80bf4884ed74ae
+size 12841237