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Release/v1.0.0b3 (#56)
* Add b6, b7 models + new weights (autoaugment) * Change version * Update readme
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‎README.md

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@@ -73,16 +73,16 @@ See the complete example of loading the model and making an inference in the Jup
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The performance of each model variant using the pre-trained weights converted from checkpoints provided by the authors is as follows:
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| Architecture | @top1* | @top5* | Weights |
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| -------------- | :----: | :----: | :-----: |
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| EfficientNetB0 | 0.7668 | 0.9312 | + |
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| EfficientNetB1 | 0.7863 | 0.9418 | + |
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| EfficientNetB2 | 0.7968 | 0.9475 | + |
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| EfficientNetB3 | 0.8083 | 0.9531 | + |
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| EfficientNetB4 | 0.8259 | 0.9612 | + |
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| EfficientNetB5 | 0.8309 | 0.9646 | + |
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| EfficientNetB6 | - | - | - |
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| EfficientNetB7 | - | - | - |
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| Architecture | @top1* |
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| -------------- | :----: |
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| EfficientNetB0 | 0.772 |
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| EfficientNetB1 | 0.791 |
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| EfficientNetB2 | 0.802 |
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| EfficientNetB3 | 0.816 |
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| EfficientNetB4 | 0.830 |
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| EfficientNetB5 | 0.837 |
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| EfficientNetB6 | 0.841 |
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| EfficientNetB7 | 0.844 |
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**\*** - topK accuracy score for converted models (imagenet `val` set)
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@@ -102,13 +102,16 @@ $ pip install -U git+https://github.com/qubvel/efficientnet
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### Installing from PyPI
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Stable release
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PyPI stable release
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```bash
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$ pip install -U efficientnet
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```
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Latest release (with keras and tf.keras support)
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PyPI latest release (with keras and tf.keras support)
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```bash
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$ pip install -U efficientnet==1.0.0b2
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$ pip install -U --pre efficientnet
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```
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## Frequently Asked Questions
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You can also optionally create the virtual environment with all the dependencies installed by adding `--make_venv=true` and operate in a self-destructing temporary location instead of the target directory by setting `--tmp_working_dir=true`.
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* **Why are B6 and B7 model variants not yet supported?**
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Weights for B6-B7 have not been made available yet, but might appear soon. Follow the [issue](https://github.com/tensorflow/tpu/issues/377) for updates.
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## Acknowledgements
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I would like to thanks community members who actively contribute to this repository:
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‎efficientnet/__version__.py

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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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VERSION = (1, 0, '0b2')
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VERSION = (1, 0, '0b3')
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__version__ = ".".join(map(str, VERSION))
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‎efficientnet/model.py

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'releases/download/efficientnet/')
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WEIGHTS_HASHES = {
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'efficientnet-b0': ('dd631faed10515e2cd08e3b5da0624b3'
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'f50d523fe69b9b5fdf037365f9f907f0',
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'e5649d29a9f2dd60380dd05d63389666'
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'1c36e1f9596e302a305f9ff1774c1bc8'),
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'efficientnet-b1': ('3b88771863db84f3ddea6d722a818719'
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'04e0fa6288869a0adaa85059094974bb',
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'5b47361e17c7bd1d21e42add4456960c'
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'9312f71b57b9f6d548e85b7ad9243bdf'),
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'efficientnet-b2': ('e78c89b8580d907238fd45f8ef200131'
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'95d198d16135fadc80650b2453f64f6c',
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'ac3c2de4e43096d2979909dd9ec22119'
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'c3a34a9fd3cbda9977c1d05f7ebcede9'),
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'efficientnet-b3': ('99725ac825f7ddf5e47c05d333d9fb62'
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'3faf1640c0b0c7372f855804e1861508',
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'e70d7ea35fa684f9046e6cc62783940b'
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'd83d16edc238807fb75c73105d7ffbaa'),
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'efficientnet-b4': ('242890effb990b11fdcc91fceb59cd74'
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'9388c6b712c96dfb597561d6dae3060a',
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'eaa6455c773db0f2d4d097f7da771bb7'
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'25dd8c993ac6f4553b78e12565999fc1'),
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'efficientnet-b5': ('c4cb66916633b7311688dbcf6ed5c35e'
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'45ce06594181066015c001103998dc67',
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'14161a20506013aa229abce8fd994b45'
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'da76b3a29e1c011635376e191c2c2d54')
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'efficientnet-b0': ('163292582f1c6eaca8e7dc7b51b01c61'
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'5b0dbc0039699b4dcd0b975cc21533dc',
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'c1421ad80a9fc67c2cc4000f666aa507'
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'89ce39eedb4e06d531b0c593890ccff3'),
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'efficientnet-b1': ('d0a71ddf51ef7a0ca425bab32b7fa7f1'
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'6043ee598ecee73fc674d9560c8f09b0',
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'75de265d03ac52fa74f2f510455ba64f'
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'9c7c5fd96dc923cd4bfefa3d680c4b68'),
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'efficientnet-b2': ('bb5451507a6418a574534aa76a91b106'
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'f6b605f3b5dde0b21055694319853086',
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'433b60584fafba1ea3de07443b74cfd3'
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'2ce004a012020b07ef69e22ba8669333'),
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'efficientnet-b3': ('03f1fba367f070bd2545f081cfa7f3e7'
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'6f5e1aa3b6f4db700f00552901e75ab9',
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'c5d42eb6cfae8567b418ad3845cfd63a'
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'a48b87f1bd5df8658a49375a9f3135c7'),
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'efficientnet-b4': ('98852de93f74d9833c8640474b2c698d'
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'b45ec60690c75b3bacb1845e907bf94f',
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'7942c1407ff1feb34113995864970cd4'
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'd9d91ea64877e8d9c38b6c1e0767c411'),
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'efficientnet-b5': ('30172f1d45f9b8a41352d4219bf930ee'
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'3339025fd26ab314a817ba8918fefc7d',
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'9d197bc2bfe29165c10a2af8c2ebc675'
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'07f5d70456f09e584c71b822941b1952'),
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'efficientnet-b6': ('f5270466747753485a082092ac9939ca'
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'a546eb3f09edca6d6fff842cad938720',
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'1d0923bb038f2f8060faaf0a0449db4b'
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'96549a881747b7c7678724ac79f427ed'),
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'efficientnet-b7': ('876a41319980638fa597acbbf956a82d'
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'10819531ff2dcb1a52277f10c7aefa1a',
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'60b56ff3a8daccc8d96edfd40b204c11'
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'3e51748da657afd58034d54d3cec2bac')
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}
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BlockArgs = collections.namedtuple('BlockArgs', [
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raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
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' as true, `classes` should be 1000')
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if weights == 'imagenet' and (model_name not in WEIGHTS_HASHES):
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raise ValueError('Pre-trained weights are only available for '
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'EfficientNet-B0 to -B5, but you requested weights for ' +
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model_name + '.')
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# Determine proper input shape
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input_shape = _obtain_input_shape(input_shape,
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default_size=default_resolution,
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# Load weights.
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if weights == 'imagenet':
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if include_top:
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file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'
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file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_autoaugment.h5'
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file_hash = WEIGHTS_HASHES[model_name][0]
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else:
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file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'
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file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5'
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file_hash = WEIGHTS_HASHES[model_name][1]
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weights_path = keras_utils.get_file(file_name,
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BASE_WEIGHTS_PATH + file_name,

‎tests/test_model.py

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PANDA_PATH = 'misc/panda.jpg'
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PANDA_ARGS = [
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(efn.EfficientNetB0, (388, 0.8347934)),
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(efn.EfficientNetB1, (388, 0.8702488)),
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(efn.EfficientNetB2, (388, 0.8227086)),
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(efn.EfficientNetB3, (388, 0.8152614)),
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(efn.EfficientNetB4, (388, 0.7353228)),
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(efn.EfficientNetB5, (388, 0.8103732)),
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(efn.EfficientNetB0, (388, 0.7587869)),
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(efn.EfficientNetB1, (388, 0.8373562)),
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(efn.EfficientNetB2, (388, 0.8569102)),
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(efn.EfficientNetB3, (388, 0.8761664)),
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(efn.EfficientNetB4, (388, 0.7342420)),
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(efn.EfficientNetB5, (388, 0.8810669)),
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(efn.EfficientNetB6, (388, 0.8667784)),
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(efn.EfficientNetB7, (388, 0.8399882)),
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]
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