diff --git a/README.md b/README.md index 463b272..dcfceff 100644 --- a/README.md +++ b/README.md @@ -31,15 +31,8 @@ AnomaVision delivers **visual anomaly detection** optimized for production deplo The result: a 15 MB model that runs at **43 FPS on CPU** and **547 FPS on GPU**, with higher AUROC than the existing best-in-class baseline. -```python -import anomavision -model = anomavision.Padim(backbone="resnet18", device="cuda") -model.fit(train_loader) # train on normal images only -scores, maps = model.predict(test_batch) # anomaly score + heatmap per image -``` ---- ## πŸš€ Quickstart @@ -112,35 +105,6 @@ uv pip install torch torchvision torchaudio --index-url https://download.pytorch python -c "import anomavision, torch; print('βœ… Ready β€”', torch.__version__)" ``` -### Python API - -```python -import torch -import anomavision -from anomavision import padim -from torch.utils.data import DataLoader - -# Dataset (normal images only) -dataset = anomavision.AnodetDataset( - image_directory_path="./dataset/bottle/train/good", - resize=(224, 224), - crop_size=(224, 224), - normalize=True, -) -loader = DataLoader(dataset, batch_size=16) - -# Train -model = anomavision.Padim(backbone="resnet18", device="cpu", feat_dim=100) -model.fit(loader) - -# Save -torch.save(model, "padim_model.pt") # full model -model.save_statistics("padim_model.pth", half=True) # compact stats-only - -# Infer -batch, *_ = next(iter(loader)) -scores, maps = model.predict(batch) -``` ### CLI @@ -168,8 +132,71 @@ anomavision train --help anomavision export --help ``` -### REST API +--- + +
+🐍 Python API +
+Use the Python API when you want to embed AnomaVision into a larger pipeline, +run it inside a notebook, or integrate it with your own data loading logic. +```python +import torch +import anomavision +from torch.utils.data import DataLoader + +# --- 1. Dataset --- +# AnodetDataset reads normal images from a folder and applies preprocessing. +# You only need train/good/ β€” no labels, no anomalous images required. +dataset = anomavision.AnodetDataset( + image_directory_path="./dataset/bottle/train/good", + resize=(224, 224), # resize before crop + crop_size=(224, 224), # center crop to this size + normalize=True, # ImageNet mean/std normalization +) +loader = DataLoader(dataset, batch_size=16) + +# --- 2. Train --- +# fit() does a single forward pass β€” no gradient updates, no epochs. +# Fits a multivariate Gaussian at each spatial position of the feature map. +# Typical training time: under 10 seconds on CPU for ~200 images. +model = anomavision.Padim( + backbone="resnet18", # or "wide_resnet50" for higher accuracy + device="cpu", # or "cuda" + feat_dim=100, # number of random feature dimensions to keep +) +model.fit(loader) + +# --- 3. Save --- +torch.save(model, "padim_model.pt") # full model (for export or further use) +model.save_statistics("padim_model.pth", half=True) # stats-only (smaller, faster to load) + +# --- 4. Infer --- +# scores: (batch_size,) β€” scalar anomaly score per image. Higher = more anomalous. +# maps: (batch_size, H, W) β€” spatial heatmap showing *where* the anomaly is. +# Pass directly to matplotlib.imshow() to visualize. +batch, *_ = next(iter(loader)) +scores, maps = model.predict(batch) +``` + +See the [FAQ](#-faq) for how to pick a classification threshold from `scores`. + +
+ + +
+🌐 REST API +
+ +Use the REST API when you want to integrate AnomaVision into an existing service, +call it from any language, or expose it on a network without installing Python on the client. + +First, start the FastAPI server (keep this terminal open): +```bash +uvicorn apps.api.fastapi_app:app --host 0.0.0.0 --port 8000 +``` + +Then send images from any client: ```python import requests @@ -180,9 +207,14 @@ print(r.json()["anomaly_score"]) # e.g. 14.3 print(r.json()["is_anomaly"]) # True / False ``` ---- +Full docs at **http://localhost:8000/docs** once the server is running. + +
+ +
+πŸ“Š Models & Performance +
-## πŸ“Š Models & Performance ### MVTec AD β€” Average over 15 Classes @@ -257,7 +289,13 @@ anomavision export \ --- -## πŸ“Ί Streaming Sources +
+ +
+πŸ“Ί Streaming Sources +
+ + Run inference on **live sources** without changing your model or code β€” just update the config: @@ -284,6 +322,11 @@ anomavision detect --config stream_config.yml ``` --- +
+ +
+βš™οΈ Configuration +
## βš™οΈ Configuration @@ -319,7 +362,13 @@ Full key reference: [`docs/config.md`](docs/config.md) --- -## πŸ”Œ Integrations +
+ +
+πŸ”Œ Integrations +
+ + | Integration | Description | |---|---| @@ -348,7 +397,13 @@ Open **http://localhost:8501** --- -## πŸ“‚ Dataset Format +
+ +
+πŸ“‚ Dataset Format +
+ + AnomaVision uses [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) layout. Custom datasets work with the same structure: @@ -378,6 +433,7 @@ dataset/ **Adaptive Gaussian post-processing** is applied to score maps after inference. The kernel is sized relative to the image resolution, which is a key factor behind the Pixel AUROC gain over baseline. --- +
## πŸ› οΈ Development @@ -545,7 +601,6 @@ More: [`docs/troubleshooting.md`](docs/troubleshooting.md) ## πŸ—ΊοΈ Roadmap - [ ] Pre-trained model zoo for all 15 MVTec classes -- [ ] Multi-class single-checkpoint model - [ ] Few-shot adaptation (5–10 anomalous examples) - [ ] Native TensorRT export in `export.py` - [ ] Pixel-level mask in REST `/predict` response diff --git a/pyproject.toml b/pyproject.toml index 44fa806..d897758 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "anomavision" -version = "3.2.2" +version = "3.3.0" description = "Advanced Anomaly Detection Environment - Production Ready" authors = [{ name = "Deep Knowledge", email = "Deepp.Knowledge@gmail.com" }] readme = "README.md" diff --git a/uv.lock b/uv.lock index b503ac6..6d2b38f 100644 --- a/uv.lock +++ b/uv.lock @@ -70,7 +70,7 @@ wheels = [ [[package]] name = "anomavision" -version = "3.2.2" +version = "3.3.0" source = { editable = "." } dependencies = [ { name = "easydict" }, @@ -3023,12 +3023,12 @@ dependencies = [ { name = "torch", version = "2.5.1+cu121", source = { registry = "https://download.pytorch.org/whl/cu121" } }, ] wheels = [ - { url = "https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:cf38267b5946b4dc3ff80ece5a1c165aa6b2a6fe8dca37bac192fcf47f658db1" }, - { url = "https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp310-cp310-win_amd64.whl", hash = "sha256:68c08cf69f7f39608f4c8d6b87fa054d7c801a19060e89d66003efbceef15641" }, - { url = "https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:9e6f03e6410cb3557978dea25fb30ce3e7c165e8377fcc8e0e1ddc700503d38b" }, - { url = "https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp311-cp311-win_amd64.whl", hash = "sha256:f9397ff9c6e8fa1b4fdc94939411ac65fe43f66d66367ee80603bc7ce10e18e2" }, - { url = "https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:5648a01f23033f15d60dc638f91c2d4c66c0a01621162471e806064acda63b70" }, - { url = "https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp312-cp312-win_amd64.whl", hash = "sha256:80400d75da5852bb5491f6259d47a163a00c2d1479ed57d3d95fde205e1b2815" }, + { url = "https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:cf38267b5946b4dc3ff80ece5a1c165aa6b2a6fe8dca37bac192fcf47f658db1" }, + { url = "https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp310-cp310-win_amd64.whl", hash = "sha256:68c08cf69f7f39608f4c8d6b87fa054d7c801a19060e89d66003efbceef15641" }, + { url = "https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:9e6f03e6410cb3557978dea25fb30ce3e7c165e8377fcc8e0e1ddc700503d38b" }, + { url = "https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp311-cp311-win_amd64.whl", hash = "sha256:f9397ff9c6e8fa1b4fdc94939411ac65fe43f66d66367ee80603bc7ce10e18e2" }, + { url = "https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:5648a01f23033f15d60dc638f91c2d4c66c0a01621162471e806064acda63b70" }, + { url = "https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp312-cp312-win_amd64.whl", hash = "sha256:80400d75da5852bb5491f6259d47a163a00c2d1479ed57d3d95fde205e1b2815" }, ] [[package]] @@ -3050,12 +3050,12 @@ dependencies = [ { name = "torch", version = "2.6.0+cu124", source = { registry = "https://download.pytorch.org/whl/cu124" } }, ] wheels = [ - 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