Add Scan layer to the new engine #29577
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Added Cumprod and Causalconv layers in new dnn engine #29579
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Updated ONNX conformance list #29560
updated ONNX conformance list with onnx version = 1.22.0
Current ONNX coverage in OpenCV ENGINE_NEW is now **72.1%**
Merge with: https://github.com/opencv/opencv_extra/pull/1398
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Extended Attention layer support #29333
Implemented present/past KV support.
Merge with: https://github.com/opencv/opencv_extra/pull/1381
Co-authored by: @Akansha-977
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dnn: implement LpPool ONNX operator #29217
## Summary
Implements the `LpPool` ONNX operator (opset 1–18), which was previously unregistered and caused a parse failure. `LpPool` computes the Lp-norm pooling: `(sum(|x|^p))^(1/p)` over a sliding window.
## Changes
- New `LpPoolLayer` in `modules/dnn/src/layers/lppool_layer.cpp`
- Supports `kernel_shape`, `strides`, `dilations`, `pads`, `auto_pad` (NOTSET/SAME_UPPER), `ceil_mode`, and `p` (default 2)
- SIMD fast paths for p=1 (abs + accumulate) and p=2 (square + accumulate + sqrt); scalar fallback for other values of p
- Registered `LpPool` dispatch entry in both `onnx_importer.cpp` (classic engine) and `onnx_importer2.cpp` (new graph engine)
- Added `LpPoolLayer` declaration to `modules/dnn/include/opencv2/dnn/all_layers.hpp`
- Registered layer class in `modules/dnn/src/init.cpp`
- Re-enabled 8 lppool conformance tests in `test_onnx_conformance.cpp` (previously in parser denylist)
- `test_lppool_2d_same_lower` added to the global conformance denylist — same known SAME_LOWER padding bug that affects `averagepool` and `maxpool`
## Testing
All applicable ONNX conformance tests pass:
| Test | Result |
|------|--------|
| test_lppool_1d_default | PASSED |
| test_lppool_2d_default | PASSED |
| test_lppool_2d_dilations | PASSED |
| test_lppool_2d_pads | PASSED |
| test_lppool_2d_same_lower | SKIPPED (known SAME_LOWER padding bug, consistent with avgpool/maxpool) |
| test_lppool_2d_same_upper | PASSED |
| test_lppool_2d_strides | PASSED |
| test_lppool_3d_default | PASSED |
Tested on: macOS (x86_64/SSE4, Rosetta 2) and Linux x86_64 (AVX2/AVX-512, GCC 13.3.0), Release build
OpenCV version: 5.0.0-pre
## Related Issues
None
---
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bound tile axis and repeats length in onnx parseTile #29345
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1385
parseTile sizes repeats_vec from the input-0 rank, then fills it from fields of the model that are never checked against that size. In the tile-1 path the axis taken from the third input indexes repeats_vec directly, and in the tile>1 path the loop writes one entry per element of the repeats tensor. A crafted ONNX with an out-of-range axis, or a repeats tensor longer than the input rank, writes past repeats_vec while loading the model through readNetFromONNX.
The fix runs axis through normalize_axis, the same helper the squeeze and concat paths in this file already use, so a negative or oversized axis is rejected before the write, and it checks the repeats length equals the input rank before the loop. Keeping both bounds in the parser puts the check next to the write instead of trusting the model to be well formed. Before, a repeats tensor shorter than the rank was silently accepted; after, it is rejected, which matches the ONNX rule that repeats carries one entry per input dimension.
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fixed Dynamic quantized linear layer error #29386
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Extended support for Resize and Split layers #29323
Merge with: https://github.com/opencv/opencv_extra/pull/1379
Key changes:
Resize layer:
_antialias_ (linear & cubic) :- PIL-style separable resampling with stretched filter support and edge-clamped, renormalized weights.
_axes_ (incl. reversed [3,2]) :- getOutShape, the scale override, and runtime _tf_crop_and_resize_ ROI parsing now map 2-element sizes/scales/roi by the axes order instead of assuming [2,3].
_keep_aspect_ratio_policy_ (not_larger/not_smaller) :- output size from min/max per-axis scale.
_half_pixel_symmetric_ :- new coordinate-transform mode + importer mapping.
_align_corners_ downsampling :- coordinate scale uses the unfloored scaled length (in−1)/(in·x_scale−1).
Split Layer:
_convertTo empty 1-D Mat:_ the empty-Mat branch collapsed a 1-D [0] to 2-D [1,0] via cv::Size(); now uses allowTransposed like the non-empty path.
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Added DISK feature extractor support #29073
closes: https://github.com/opencv/opencv/issues/27083
Merge with: https://github.com/opencv/opencv_extra/pull/1368/
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Caffe importer cleanup #28678
Merge with: https://github.com/opencv/opencv_extra/pull/1324
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Update default YuNet model to new dynamic inputs #29107
Update the default model in `face_detect.py` and `face_detect.cpp` to
`face_detection_yunet_2026may.onnx`, which has symbolic `height`/`width` input dims.
## Changes
- `samples/dnn/face_detect.py`: update default `--face_detection_model` to `face_detection_yunet_2026may.onnx`
- `samples/dnn/face_detect.cpp`: update default `fd_model` to `face_detection_yunet_2026may.onnx`
Companion PR :
- https://github.com/opencv/opencv_zoo/pull/310
- https://github.com/opencv/opencv_extra/pull/1373
Closes : https://github.com/opencv/opencv/issues/28769
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Moved geometry transformations from imgproc to 3d, future geometry module #29101
The first step of 2d geometry operations migration to the future geometry module.
I created 2d.hpp to isolate the moved functions for now. I propose to create geometry.hpp when the module is renamed and include all things there.
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4126
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Added SDPA layer (Scaled Dot Product Attention) #29104
Merge with: https://github.com/opencv/opencv_extra/pull/1374
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dnn int8 optimization #29079
all_layers.hpp
- Add float_input flag to Conv2Int8Params and Conv2Int8Layer to let the first conv accept raw FP32 input and quantize internally.
graph_fusion_qdq.cpp :
- Fuse DQ → Sigmoid → QL into SigmoidInt8, Similarly for MAxPool.
- Fuse the input QuantizeLinear node into the first Conv2Int8.
conv2_int8_layer.cpp
- Add quantizeInterleaveBlock()
conv2_int8_kernels.simd.hpp
- Add spatial tiling to both convInt8BlockVNNI and convInt8BlockDepthwise: splits output pixels into tiles so total task count is N × ngroups × Kblk × ntiles, fully utilizing all threads even when the channel count is small.
elementwise_layers.cpp
- Widen CV_Assert to accept CV_8U in addition to CV_8S.
eltwise2_int8_layer.cpp
- Add QLinearMul support: new Mul math path for both signed and unsigned int8.
- Add numpy-style broadcast support so QLinearMul / QLinearAdd with scalar
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Add KV cache with paged attention and prefetch #29127
Closes: https://github.com/opencv/opencv/issues/27159
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FP32 KV Cache #28840
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1348
## This PR introduces basic (Paged ) KV-Cache to use on CPU
### Summary:
1. To ensure proper gemm-prepacking,
1.1. The Page Size of Key Cache is currently hardcoded as `FAST_GEMM_F32_NR`(which is 8, 12 or 16 depending on CPU architecture)
1.2. The Page Size of Values Cache is hardcoded as `FAST_GEMM_F32_PACKED_STRIDE_K`
2. there are two phases supported - prefill & generate.
2.1. prefill grows cache by `N` tokens and is allowed **only** for empty cache
2.2. generate grows cache by 1 token.
2.3. **Improtant**: it is currently not allowed to grow non-empty cache by more than one token at a time (thisbehaviour is sufficient for normal LLM querying, but should be extended if we want to implement speculative decoding)
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dnn: skip Conv2Int8 fusion for grouped convs with Kg<8 (#28798) #28920
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1360
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---
Fixes#28798.
The YuNet 2023mar int8 model from opencv_zoo doesn't detect anything on 5.x. With `--score_threshold
0.05` the detector still returns no boxes, so it's not just a confidence drop — the network output is
broken.
After dumping intermediate tensors and comparing against onnxruntime, the `obj_*` branches collapse to
~0 (saturated int8 `-128`), and the `cls_*` branches saturate the other way. Final score is `cls * obj`
so nothing ever crosses threshold.
The cause is in `Conv2Int8`'s VNNI kernel. It processes `K0 = 8` output channels per SIMD iteration and
writes them as one `K0`-wide block to `out + (n*K1 + k1)*planesize` with `k1 = k_base / K0`. When
`ngroups > 1` and `Kg = K/ngroups < K0`, consecutive groups share the same `k1` slot and overwrite each
other — only the last group's result survives. YuNet has six `1x1x3x3` depthwise conv heads (`Kg = 1`),
which is exactly this case.
The unfused `DequantizeLinear → Conv2 → QuantizeLinear` path is fine. The minimal fix here is to skip
the rewrite to `Conv2Int8` when `Kg < 8` so we fall back to the float path. A proper depthwise int8
kernel can be added later as a separate optimization.
Verified with `samples/dnn/face_detect.py` on Lena:
Before:
AssertionError: Cannot find a face in samples/data/lena.jpg
After:
Face 0, top-left coordinates: (204, 187), box width: 149, box height 212, score: 0.89
Cross-check vs onnxruntime: `cls_8` mean `0.673` (was `0.93`, ORT `0.673`), `obj_8` max `0.0039` (was
`0`, ORT `0.0039`).
The regression test is a single 16-group depthwise `QLinearConv` with non-zero `x_zp`, added to
`Quantized_Convolution`. Test data is in opencv_extra on the matching branch (~9 KB total).
<img width="1864" height="1060" alt="image" src="https://github.com/user-attachments/assets/a1b15c64-6b84-42b1-86a4-1d55474cb38c" />
fix int32 overflow in shape_utils::total() for large tensors #29093
Fixes https://github.com/opencv/opencv/issues/24914
### Problem
When running inference with a ConvTranspose (deconvolution) layer on large inputs
(e.g. 4864×4864 with 30 channels), the DNN module crashes with:
OpenCV net_impl.cpp: error:
(expected: 'total(ints[i]) > 0'), where
'total(ints[i])' is -1455947776
must be greater than
'0' is 0
The root cause is `shape_utils::total()` which returns `int` (32-bit signed).
`ENGINE_CLASSIC` catches this via `CV_CheckGT` and throws.
`ENGINE_NEW` was silently bypassing the check — the overflow in `total()` itself
was never addressed.
### Changes
**`modules/dnn/include/opencv2/dnn/shape_utils.hpp`** — root fix
- Changed return type of both `total()` overloads from `int` to `size_t`
- Changed accumulator from `int elems = 1` to `size_t elems = 1`
**`modules/dnn/src/net_impl.cpp`**
- Updated `CV_CheckGT(total(...), 0)` to `CV_CheckGT(total(...), (size_t)0)`
to match the new return type
**`modules/dnn/src/net_impl2.cpp`**
- Added the same `CV_CheckGT` shape validation that `ENGINE_CLASSIC` has in
`net_impl.cpp:1333-1337` — `ENGINE_NEW` was missing this check entirely
**`modules/dnn/src/legacy_backend.hpp`**
- Removed the now-incorrect `(int)` cast in `CV_CheckEQ` — both sides are
now `size_t`
### Test
Added `Net.ShapeUtils_total_no_int32_overflow` in `modules/dnn/test/test_misc.cpp`:
- The shape [1920 × 1,478,656] is the exact im2col buffer from the bug report.
EXPECT_EQ verifies total() returns the correct size_t value 2,839,019,520.
EXPECT_LT documents that casting it to int wraps to -1,455,947,776 — the
value that caused the original crash.
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[Bug Fix] Slice2 empty-range crash in new DNN engine#29029
Closes: https://github.com/opencv/opencv/issues/29023
Merge with: https://github.com/opencv/opencv_extra/pull/1363
## Summary
ENGINE_NEW asserted `CV_Assert(outsz >= 0)` in `slice2_layer.cpp` when an ONNX Slice op had `start > end` with positive step. Per ONNX spec, such slices are valid and
produce an empty-range output (size 0). The hard assertion crashed SwinIR inference on ENGINE_NEW.
## Fix
- Replaced the fatal assertion with a graceful clamp: when `outsz < 0`, set `outsz = 0` and `end = start`.
- Moved `allEnds[axis] = end` to after the clamp so downstream shape inference receives a consistent value (the previous order propagated un-clamped garbage end values
into `normalize_axis()`, causing SIGSEGV).
## Test
Adds `Reproducibility_SwinIR_ONNX` accuracy test in `test_model.cpp`. Test data registered in opencv_extra PR with matching branch name `fix/dnn-slice2-empty-range`.
- Without fix: 3/3 FAIL with `slice2_layer.cpp:124: (-215:Assertion failed) outsz >= 0`
- With fix: 3/3 PASS (CPU, OCL, OCL_FP16)
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SIMD Kernel speedup for DNN layers #28889
This PR add following speedups for Grounding Dino tiny model.
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- |
| **Grounding Dino Tiny** | 3130 ms | 1872.06 ms| 1800.18 ms|
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KleidiCV update to verison 26.03 #28744
KleidiCV release: https://gitlab.arm.com/kleidi/kleidicv/-/releases/26.03
Tuned DNN test threshold as resize linear produces slightly different result with the new KleidiCV version.
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Port of the 4.x patch to 5.x. Registers a new
ConsecutiveTransposePairsSubgraph in onnx_graph_simplifier.cpp's
simplifySubgraphs() so that consecutive Transpose nodes whose composed
permutation is identity are eliminated at the ONNX proto level. This
runs before either engine takes over, so both ENGINE_CLASSIC and
ENGINE_NEW benefit.
Equivalent optimizations exist in tf2onnx (TransposeOptimizer._transpose_handler)
and ONNX Runtime (HandleTransposeImpl, 'Permutations cancel' branch).
Fixed subgraph name scoping in new DNN engine #28971
closes: https://github.com/opencv/opencv/issues/23663, https://github.com/opencv/opencv/issues/19977
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1362
### Pull Request Readiness Checklist
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Extend attention fusion for runtime QK scale and add MatMul to Gemm rewriter #28957
### Pull Request Readiness Checklist
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fixes a bug in ENGINE_NEW of incorrect fusion of conv+relu+batchnorm #28722
### Pull Request Readiness Checklist
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1325
Closes Issue https://github.com/opencv/opencv/issues/28689
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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Patch to opencv_extra has the same branch name.
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fix getUnconnectedOutLayers() in new engine #28982
Solves https://github.com/opencv/opencv/issues/26491
### Pull Request Readiness Checklist
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Fixed dequantizelinear slicing bug #28966
closes: https://github.com/opencv/opencv/issues/25999
### Pull Request Readiness Checklist
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Added fully functional convTranspose layer to new DNN engine #27560
Closes https://github.com/opencv/opencv/issues/26307
### Pull Request Readiness Checklist
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Extend several operations to support block layout #28839
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1347
After this PR, we observed the following improvements in the listed models:
Model | Before (ENGINE_NEW) | After (ENGINE_NEW) | ENGINE_ORT | % Improvement (Before v/s After)
-- | -- | -- | -- | --
Face_Paint | 514.68ms | 384.40ms | 394.38ms | 25.31%
BlazeFace | 1.03ms | 0.83ms | 0.66ms | 19.42%
**Device details:**
Model name: Intel(R) Core(TM) i9-14900KS, x86_64, 32 Cores, Ubuntu 22.04.5 LTS
Operations covered:
- [x] Pad
- [x] Resize
- [x] Reshape
- [x] Shape
- [x] InstanceNorm
- [x] GroupNorm
### Pull Request Readiness Checklist
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Added Attention fusion and thin gemm support #28859
Merge with: https://github.com/opencv/opencv_extra/pull/1350
Performance numbers after these optimizations:
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- |
| **BERT** |26.3 ms| 9.15 ms| 9.13 ms|
| **ViT** | 79.65 ms| 63.23 ms| 32.3 ms|
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Add Gemma3 tokenizer support for dnn #28837
- Adds Gemma3 tokenizer support
- Implements character-level BPE
- Adds 6 tests covering English, phrase, mixed case, numbers, special tokens, and encode/decode
- add gemma3_inference.py
Merge with:
- **Companion PR** : https://github.com/opencv/opencv_extra/pull/1346
- forward pass bug in gemma3_inference.py : https://github.com/opencv/opencv/pull/28836
### Pull Request Readiness Checklist
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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Added QLinear layer support #28811
closes: https://github.com/opencv/opencv/issues/26310
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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Abhishek Gola
Teddy-Yangjiale
Alexander Smorkalov
Prasadayus
Saravana Balaji Mohan Balaji
uwezkhan
Savya Sanchi Sharma
omrope79
Alexander Smorkalov
Varun Jaiswal
vrooomy
Prasad Ayush Kumar
nklskyoy
sujal
Naresh
Kevin Lin
Dmitry Kurtaev