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 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,5 +1,88 @@
----
-license: other
-license_name: all-rights-reserved
-license_link: LICENSE
----
+---
+license: apache-2.0
+tags:
+- image-classification
+- vision
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: outputs
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9107332624867163
+---
+
+<!-- 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. -->
+
+# outputs
+
+This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the [PETA dataset](http://mmlab.ie.cuhk.edu.hk/projects/PETA_files/Pedestrian%20Attribute%20Recognition%20At%20Far%20Distance.pdf) dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2170
+- Accuracy: 0.9107
+
+## Model description
+
+More information needed
+
+#### How to use
+
+You can use this model with Transformers *pipeline* .
+
+```python
+from transformers import pipeline
+gender_classifier = pipeline(model="NTQAI/pedestrian_gender_recognition")
+image_path = "abc.jpg"
+
+results = gender_classifier(image_path)
+print(results)
+```
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 1337
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.5193        | 1.0   | 2000  | 0.3346          | 0.8533   |
+| 0.337         | 2.0   | 4000  | 0.2892          | 0.8778   |
+| 0.3771        | 3.0   | 6000  | 0.2493          | 0.8969   |
+| 0.3819        | 4.0   | 8000  | 0.2275          | 0.9100   |
+| 0.3581        | 5.0   | 10000 | 0.2170          | 0.9107   |
+
+
+### Framework versions
+
+- Transformers 4.24.0.dev0
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1
+
+### Contact information
+For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).

all_results.json

@@ -0,0 +1,12 @@
+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.9107332624867163,
+    "eval_loss": 0.21704278886318207,
+    "eval_runtime": 12.0095,
+    "eval_samples_per_second": 235.064,
+    "eval_steps_per_second": 29.393,
+    "train_loss": 0.4213516190290451,
+    "train_runtime": 1040.551,
+    "train_samples_per_second": 76.858,
+    "train_steps_per_second": 9.61
+}

config.json

@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "microsoft/beit-base-patch16-224-pt22k-ft22k",
+  "architectures": [
+    "BeitForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auxiliary_channels": 256,
+  "auxiliary_concat_input": false,
+  "auxiliary_loss_weight": 0.4,
+  "auxiliary_num_convs": 1,
+  "drop_path_rate": 0.1,
+  "finetuning_task": "image-classification",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Female",
+    "1": "Male"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Female": "0",
+    "Male": "1"
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_scale_init_value": 0.1,
+  "model_type": "beit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "out_indices": [
+    3,
+    5,
+    7,
+    11
+  ],
+  "patch_size": 16,
+  "pool_scales": [
+    1,
+    2,
+    3,
+    6
+  ],
+  "problem_type": "single_label_classification",
+  "semantic_loss_ignore_index": 255,
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0.dev0",
+  "use_absolute_position_embeddings": false,
+  "use_auxiliary_head": true,
+  "use_mask_token": false,
+  "use_mean_pooling": true,
+  "use_relative_position_bias": true,
+  "use_shared_relative_position_bias": false,
+  "vocab_size": 8192
+}

emissions.csv

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+timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region
+2023-01-06T04:21:59,6f267fc3-7da5-4884-830d-71f0c8057ca4,codecarbon,1043.5702650547028,0.07675515188614081,0.10504157083518204,Vietnam,VNM,hanoi,N,,

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.9107332624867163,
+    "eval_loss": 0.21704278886318207,
+    "eval_runtime": 12.0095,
+    "eval_samples_per_second": 235.064,
+    "eval_steps_per_second": 29.393
+}

model.onnx

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preprocessor_config.json

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+{
+  "crop_size": 224,
+  "do_center_crop": false,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "BeitFeatureExtractor",
+  "image_mean": [
+    0.5,
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+  ],
+  "image_std": [
+    0.5,
+    0.5,
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+  "reduce_labels": false,
+  "resample": 2,
+  "size": 224
+}

pytorch_model.bin

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train_results.json

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+{
+    "epoch": 5.0,
+    "train_loss": 0.4213516190290451,
+    "train_runtime": 1040.551,
+    "train_samples_per_second": 76.858,
+    "train_steps_per_second": 9.61
+}

training_args.bin

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