paolinox/mobilenet-FT-food101

README.md

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+---
+license: other
+base_model: google/mobilenet_v2_1.0_224
+tags:
+- generated_from_trainer
+datasets:
+- food101
+metrics:
+- accuracy
+model-index:
+- name: mobilenet-finetuned-food101
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: food101
+      type: food101
+      config: default
+      split: train[:5000]
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.821
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# mobilenet-finetuned-food101
+
+This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the food101 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5518
+- Accuracy: 0.821
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 128
+- eval_batch_size: 128
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 6    | 1.9575          | 0.153    |
+| 1.9536        | 2.0   | 12   | 1.8509          | 0.265    |
+| 1.9536        | 3.0   | 18   | 1.7003          | 0.451    |
+| 1.7915        | 4.0   | 24   | 1.5181          | 0.578    |
+| 1.4994        | 5.0   | 30   | 1.3609          | 0.631    |
+| 1.4994        | 6.0   | 36   | 1.2321          | 0.669    |
+| 1.2203        | 7.0   | 42   | 1.0696          | 0.69     |
+| 1.2203        | 8.0   | 48   | 0.9676          | 0.723    |
+| 1.0215        | 9.0   | 54   | 0.8888          | 0.729    |
+| 0.8462        | 10.0  | 60   | 0.8380          | 0.74     |
+| 0.8462        | 11.0  | 66   | 0.7461          | 0.778    |
+| 0.744         | 12.0  | 72   | 0.6724          | 0.792    |
+| 0.744         | 13.0  | 78   | 0.7314          | 0.769    |
+| 0.6496        | 14.0  | 84   | 0.6831          | 0.77     |
+| 0.6143        | 15.0  | 90   | 0.5937          | 0.81     |
+| 0.6143        | 16.0  | 96   | 0.6217          | 0.793    |
+| 0.5468        | 17.0  | 102  | 0.5965          | 0.788    |
+| 0.5468        | 18.0  | 108  | 0.5944          | 0.813    |
+| 0.5428        | 19.0  | 114  | 0.5869          | 0.812    |
+| 0.5193        | 20.0  | 120  | 0.5565          | 0.82     |
+| 0.5193        | 21.0  | 126  | 0.6155          | 0.803    |
+| 0.4902        | 22.0  | 132  | 0.5685          | 0.817    |
+| 0.4902        | 23.0  | 138  | 0.6097          | 0.789    |
+| 0.4869        | 24.0  | 144  | 0.6002          | 0.8      |
+| 0.4745        | 25.0  | 150  | 0.5569          | 0.814    |
+| 0.4745        | 26.0  | 156  | 0.5414          | 0.821    |
+| 0.4653        | 27.0  | 162  | 0.5806          | 0.807    |
+| 0.4653        | 28.0  | 168  | 0.5663          | 0.807    |
+| 0.4543        | 29.0  | 174  | 0.5412          | 0.825    |
+| 0.4575        | 30.0  | 180  | 0.5518          | 0.821    |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "google/mobilenet_v2_1.0_224",
+  "architectures": [
+    "MobileNetV2ForImageClassification"
+  ],
+  "classifier_dropout_prob": 0.2,
+  "depth_divisible_by": 8,
+  "depth_multiplier": 1.0,
+  "expand_ratio": 6,
+  "finegrained_output": true,
+  "first_layer_is_expansion": true,
+  "hidden_act": "relu6",
+  "id2label": {
+    "0": "beignets",
+    "1": "bruschetta",
+    "2": "chicken_wings",
+    "3": "hamburger",
+    "4": "pork_chop",
+    "5": "prime_rib",
+    "6": "ramen"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "beignets": 0,
+    "bruschetta": 1,
+    "chicken_wings": 2,
+    "hamburger": 3,
+    "pork_chop": 4,
+    "prime_rib": 5,
+    "ramen": 6
+  },
+  "layer_norm_eps": 0.001,
+  "min_depth": 8,
+  "model_type": "mobilenet_v2",
+  "num_channels": 3,
+  "output_stride": 32,
+  "problem_type": "single_label_classification",
+  "semantic_loss_ignore_index": 255,
+  "tf_padding": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2"
+}

model.safetensors

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+oid sha256:ce7b82d2fd228b55d70a63370bb3468087cfdfee4d5ea0e06344e6a1c605bdb4
+size 9105836

preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "MobileNetV2ImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 256
+  },
+  "use_square_size": false
+}

training_args.bin

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+size 4600