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747451d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | # <a>Object detection STM32 model zoo</a>
Before you start using this project, it's important to understand the supported dataset names and formats. Please note that for all the training, evaluation and quantization services, it is expected to have a dataset in TFS Tensorflow format. For the object detection use case, the `get_dataset` API call takes care of the conversion of your dataset automatically depending on the `dataset_name` and `format` attributes.
The `dataset` section and its attributes are shown in the YAML code below.
```yaml
dataset:
format: pascal_voc
dataset_name: pascal_voc # Dataset name. Defaults to "<unnamed>".
class_names: [ aeroplane,bicycle,bird,boat,bottle,bus,car,cat,chair,cow,diningtable,dog,horse,motorbike,person,pottedplant,sheep,sofa,train,tvmonitor ] # Names of the classes in the dataset.
data_dir: ./datasets/pascal_voc/tmp/ # Path to the tmp directory before the split.
train_images_path: /local/datasets/VOC0712/JPEGImages/ # Path to the root directory of the img before split.
train_xml_dir: /local/datasets/VOC0712/Annotations # Path to the root directory of the xml annotations
training_path: <training-set-root-directory> # Path to the root directory of the training set.
validation_path: <validation-set-root-directory> # Path to the root directory of the validation set.
validation_split: 0.2 # Training/validation sets split ratio.
test_path: <test-set-root-directory> # Path to the root directory of the test set.
quantization_path: <quantization-set-root-directory> # Path to the root directory of the quantization set.
quantization_split: # Quantization split ratio.
seed: 123 # Random generator seed used when splitting a dataset.
```
The `dataset_name` attribute is required and serves to specify the dataset you are using. This can be a well-known dataset like coco, pascal_voc, or a custom_dataset if you have your own data and it follows the logic below:
| Dataset Name | Allowed Formats | Description |
|------------------|-------------------------|----------------------------------------------------------------------------------------------|
| `coco` | `coco`, `tfs` | Native COCO format or TFS TensorFlow format |
| `pascal_voc` | `pascal_voc`, `tfs` | Native Pascal VOC format or TFS TensorFlow format |
| `darknet_yolo` | `darknet_yolo`, `tfs` | Native Darknet YOLO format or TFS TensorFlow format |
| `custom_dataset` | `tfs` | Only TFS TensorFlow format; in case the dataset is already converted before evaluation |
Depending on the `dataset_name`, the dataset loader will check the `format` to determine if it is necessary to convert the dataset to the final **TFS TensorFlow format**. These two parameters are mandatory if the operation mode is **training**, **evaluation** and **quantization**.
The `format` attributes defines the annotation format of your dataset. This must match the format of your dataset annotations.
It serves to check whether your dataset is in its original format or in TFS TensorFlow format.
This determines whether it is needed to convert the dataset to the required TFS format or not. It accepts the following values:
* `tfs`: If the dataset is a TensorFlow Object Detection API format.
* `coco`: If the dataset is in COCO dataset format (JSON annotations).
* `pascal_voc`: If the dataset is in Pascal VOC XML annotation format.
* `darknet_yolo`: If the dataset is in YOLO Darknet text file annotations.
Depending on the `format` value, some additional attributes should be defined in the dataset section:
- If the `format` is set to **coco**, the following attributes should be set:
* The `data_dir`: Required, refers to the temporary path where the TFS files will be generated.
* The `train_images_path`: Required, refers to the path of the training subset directory where the images are located.
* The `train_annotations_path`: Required, refers to the path of the training subset json file of the annotations.
* The `val_images_path`: Optional, refers to the path of the validation subset directory where the images are located.
* The `val_annotations_path`: Optional, refers to the path of the training subset json file of the annotations.
- If the `format` is set to **pascal_voc**, the following attributes should be set:
* The `data_dir`: Required, refers to the temporary path where the TFS files will be generated.
* The `train_images_path`: Required, refers to the path of the training subset directory where the images are located.
* The `train_xml_dir`: Required, refers to the path of the training subset directory containing the xml files of the annotations.
* The `val_images_path`: Optional, refers to the path of the validation subset directory where the images are located.
* The `val_xml_dir`: Optional, refers to the path of the training subset directory containing the xml files of the annotations.
- If the `format` is set to **darknet_yolo**, the following attributes should be set:
* The `data_dir`: Required, refers to the path of the directory containing the txt files of the annotations along with the images.
The state machine below describes the process of dataset loading for object detection use case.
```
dataset_name
|
|
+----------------------------------+--------------------------+-------------------------------+
| | | |
| | | |
coco pascal_voc darknet_yolo "custom_dataset"
| | | |
| | | |
+-----+------------+ +-----+-----------+ +-------+-------+ +-------+-------+
| | | | | | | |
supported unsupported supported unsupported supported unsupported supported unsupported
format: format format format format: format format format
| | | |
+---+-----+ +---+---+ +----+-----+ |
| | | | | | |
coco tfs pascal_voc tfs darknet_yolo tfs tfs
| | | | | | (Custom dataset
| | | | | | should be used
| | | | | | if the conversion
| dataset.format=tfs | dataset.format=tfs | dataset.format=tfs has already been
| (already TFS) | (already TFS) | (already TFS) done in a previous
| | | | | | training or eval)
| | | | | | |
| load TFS directly | load TFS directly | load TFS directly load TFS directly
| | | |
| | | |
dataset.format=coco dataset.format=pascal_voc dataset.format=darknet_yolo |
(needs conversion) (needs conversion) (needs conversion) |
| | | |
v v v |
convert coco to tfs convert pascal_voc to tfs convert darknet yolo to tfs |
| | | |
+-------------------------+-------------------------------+---------------------------+
|
Dataset in TFS format
(used for)
+---------------------+-----------------------+
| | |
training evaluation quantization
```
## Dataset Configuration
### Details of Required / Optional Attributes per `(dataset_name, format)`
---
### 1. `dataset_name = coco`
**Supported `format` values:**
- `tfs`
- `coco`
#### 1.a `format = tfs`
- Dataset is already in **TFS TensorFlow** format.
- Loader reads TFS files directly.
**Required attributes**
- `data_dir`
→ Temporary path where the TFS files are located.
---
#### 1.b `format = coco`
- Dataset is in **COCO JSON** annotation format and must be converted to TFS.
**Required attributes**
- `data_dir`
→ Temporary path where the TFS files will be generated.
- `train_images_path`
→ Path to training images directory.
- `train_annotations_path`
→ Path to training subset COCO JSON annotations file.
**Optional attributes**
- `val_images_path`
→ Path to validation images directory.
- `val_annotations_path`
→ Path to validation subset COCO JSON annotations file.
**Conversion flow**
1. Read images/annotations from `train_*` (and optionally `val_*`).
2. Generate TFS TensorFlow records into `data_dir`.
3. Load resulting TFS dataset for training / evaluation / quantization with the specified split ratios.
---
### 2. `dataset_name = pascal_voc`
**Supported `format` values:**
- `tfs`
- `pascal_voc`
#### 2.a `format = tfs`
- Dataset is already in **TFS TensorFlow** format.
- Loader reads TFS files directly.
**Required attributes**
- `data_dir`
→ Temporary path where the TFS files are located.
---
#### 2.b `format = pascal_voc`
- Dataset is in **Pascal VOC XML** annotation format and must be converted.
**Required attributes**
- `data_dir`
→ Temporary path where the TFS files will be generated.
- `train_images_path`
→ Path to training images directory.
- `train_xml_dir`
→ Path to directory containing training XML annotation files.
**Optional attributes**
- `val_images_path`
→ Path to validation images directory.
- `val_xml_dir`
→ Path to directory containing validation XML annotation files.
**Conversion flow**
1. Read images/annotations from `train_*` (and optionally `val_*`).
2. Generate TFS TensorFlow records into `data_dir`.
3. Load resulting TFS dataset for training / evaluation / quantization.
---
### 3. `dataset_name = darknet_yolo`
**Supported `format` values:**
- `tfs`
- `darknet_yolo`
#### 3.a `format = tfs`
- Dataset is already in **TFS TensorFlow** format.
- Loader reads TFS files directly.
**Required attributes**
- `data_dir`
→ Temporary path where the TFS files are located.
---
#### 3.b `format = darknet_yolo`
- Dataset is in **YOLO Darknet text** annotation format and must be converted.
**Required attributes**
- `data_dir`
→ Path to the directory containing:
- the `.txt` annotation files
- the corresponding images
> No separate train/val split paths are specified. By convention, `data_dir` contains both the `.txt` files and images to be converted.
**Conversion flow**
1. Parse YOLO `.txt` annotations and corresponding images in `data_dir`.
2. Generate TFS TensorFlow records.
3. Load resulting TFS dataset for training / evaluation / quantization.
---
### 4. `dataset_name = "custom_dataset"`
**Supported `format` values:**
- `tfs`
This case assumes:
- The user has already produced a **TFS TensorFlow dataset** externally or from a previous operation.
- The loader only reads the TFS dataset (no conversion is performed).
**Required / optional attributes**
- Depend on your custom TFS dataset layout (not defined here).
- At minimum, paths pointing to the TFS TFRecord files (train/val) must be provided according to the specific tool’s configuration schema.
---
### Operation Modes and Mandatory Parameters
For the following operation modes:
- `training`
- `evaluation`
- `quantization`
The following parameters are **mandatory**:
- `dataset_name`
- `format` |